Methods and apparatus to utilize minimum cross entropy to calculate granular data of a region based on another region for media audience measurement

ABSTRACT

Methods and apparatus for calculating granular data of an audience of a target region. An example method includes utilizing minimum cross entropy to apportion aggregate demographics data and aggregate behavioral data of the target region based on granular data of a source region. The example further includes reducing an amount of computer memory resources by calculating the granular data of the audience without collecting person-specific data from audience members of the target region, the granular data of the audience including (A) a count of the audience of the target region satisfying a behavioral constraint and a first demographic constraint and (B) a count of the audience of the target region satisfying the behavioral constraint and a second demographic constraint, the calculated granular data based on the aggregate demographics data of the target region, the aggregate behavioral data of the target region, and the granular data of the source region.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 15/055,257, filed Feb. 26, 2016, and granted as U.S. Pat. No.9,800,928 on Oct. 24, 2017. U.S. patent application Ser. No. 15/055,257is hereby incorporated herein by reference in its entirety. Priority toU.S. patent application Ser. No. 15/055,257 is claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement, and, moreparticularly, to utilizing minimum cross entropy to calculate granulardata of a region based on another region for media audience measurement

BACKGROUND

Audience measurement entities often collect demographic information(e.g., age, race, gender, income, education level, etc.) of a populationby having members of the population complete a survey (e.g.,door-to-door, mail, online, etc.).

Some audience measurement entities or other entities also collectbehavioral data (e.g., viewing data and/or tuning data for televisionprogramming, advertising, movies, etc.) from households of a population(e.g., upon obtaining consent from the households). In some instances,the audience measurement entities collect viewing data (e.g., datarelated to media viewed by a member of the household) from each memberof the household. To identify which household member is exposed todisplayed media, the audience measurement entities often employ meters(e.g., personal people meters) to monitor the members and/or mediapresentation devices (e.g., televisions) of the household.

Some audience measurement entities may also collect tuning data frommedia presentation device (e.g., set-top boxes) of households of apopulation. For example, the media presentation device may record tuningdata that is associated with tuning events of the media presentationdevice (e.g., turning a set-top box on or off, changing a channel,changing a volume), and the audience measurement entities may associatethe collected tuning data with information associated with the householdat which the media presentation device is located.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which aggregatedata of a target region and granular data of a source region may becollected to utilize minimum cross entropy to calculate granular data ofa media audience of the target region in accordance with the teachingsof this disclosure.

FIG. 2 is a block diagram of an example implementation of thedemographics estimator of FIG. 1 that is to utilize the minimum crossentropy to calculate granular data of the target region of FIG. 1.

FIG. 3 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example demographicsestimator of FIGS. 1 and/or 2 to determine the granular data of thetarget region of FIG. 1.

FIG. 4 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example target regioncalculator of FIG. 2 to determine the granular data of the target regionof FIG. 1.

FIG. 5 is a block diagram of an example environment for online mediacampaign measurement in which aggregate data of a target region andgranular data of a source region may be collected to determine granulardata of the target region in accordance with the teachings of thisdisclosure.

FIG. 6 is a block diagram of an example environment in which the examplemedia presentation device of FIG. 5 reports audience impressions ofmedia to impression collection entities to facilitate audiencemeasurement for media.

FIG. 7 is an example communication flow diagram illustrating an examplemanner in which the audience measurement entity of FIGS. 5 and 6 and adatabase proprietor of FIG. 6 collect data from the example mediapresentation device of the source region of FIG. 5.

FIG. 8 is a block diagram of an example implementation of thedemographics estimator of FIG. 5 that is to determine the granular dataof the target region of the online media environment of FIG. 5.

FIG. 9 is a block diagram of an example processor system structured toexecute the example machine readable instructions represented by FIGS. 3and/or 4 to implement the demographics estimator of FIGS. 1, 2, 5 and/or8.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Audience measurement entities (AMEs) and other entities measurecomposition and size of audiences consuming media to produce ratings ofthe media. Ratings may be used by advertisers and/or marketers todevelop strategies and plans to purchase advertising space and/or indesigning advertising campaigns. Additionally, media producers and/ordistributors may use the ratings to determine how to set prices foradvertising space and/or to make programming decisions. To measure thecomposition and size of an audience, AMEs (e.g., The Nielsen Company(US), LLC®) track audience members' exposure to media and associatedemographics data, demographics information and/or demographics of theaudience members (e.g., age, gender, race, education level, income,etc.) with the exposed media. Demographics data of an audience memberand/or an audience associated with exposed media may include a pluralityof characteristics of the audience member and/or the audience as awhole.

As used herein, a demographic characteristic in demographics data isreferred to as a “demographic dimension.” For example, demographicdimensions may include age, gender, age and gender, income, race,nationality, geographic location, education level, religion, etc. Ademographic dimension may include, be made up of and/or be divided intodifferent groupings.

As used herein, each grouping of a demographic dimension is referred toas a “demographic marginal” (also referred to herein as a “demographicgroup” and/or a “demographic bucket”). For example, a “gender”demographic dimension includes a “male” demographic marginal and a“female” demographic marginal.

As used herein, a “demographic constraint” refers to a demographicmarginal or a combination of independent demographic marginals ofinterest (e.g., a combination of demographic marginals of differentrespective demographic dimensions, demographic joint-marginals ordistributions). An example demographic constraint includes a marginalfrom an “age/gender” demographic dimension. Another example demographicconstraint includes a combination of a marginal from a race demographicdimension, a marginal from an “age/gender” demographic dimension, and amarginal from an “education level” demographic dimension (e.g., aLatina, 18-45 year-old male, and a master's degree).

To obtain demographics data of audience members and associate exposedmedia with demographics data of its audience, AMEs often enlistpanelists and/or panelist households to participate in measurementpanels. In some such examples, media exposure and/or demographics dataassociated with the panelists is collected and used to project a sizeand demographic makeup of a population. The panelists providedemographics data to the AMEs via, for example, self-reporting to theAMEs, responses to surveys, consenting to the AMEs obtainingdemographics data from database proprietors (e.g., Facebook, Twitter,Google, Yahoo!, MSN, Apple, Experian, etc.), etc.

In some audience measurement systems, panelists consent to AMEs or otherentities collecting exposure data by measuring exposure of the paneliststo media (e.g., television programming, radio programming, onlinecontent, programs, advertising, etc.). As used herein, “exposure data”refers to information pertaining to media exposure events presented viaa media presentation device (e.g., a television, a stereo, a speaker, acomputer, a portable device, a gaming console, an online mediapresentation device, etc.) of a household (e.g., a panelist household)and associated with a person and/or a group of persons of the household(e.g., panelist(s), member(s) of the panelist household). For example,exposure data includes information indicating that a panelist is exposedto particular media if the panelist is present in a room in which themedia is being presented. To enable the AMEs to collect such exposuredata, the AMEs typically provide panelists and/or panelist householdswith meter(s) that monitor media presentation devices (e.g.,televisions, stereos, speakers, computers, portable devices, gamingconsoles, and/or online media presentation devices, etc.).

Enlisting and retaining panelists for audience measurement can be adifficult and costly process for AMEs. For example, AMEs must carefullyselect and screen panelists for particular characteristics so that apopulation of the panelists is representative of the population as awhole. Further, panelists must diligently perform specific tasks toenable the collected demographics and exposure data to accuratelyreflect the panelist activities. For example, to identify that apanelist is exposed to a particular media, some AMEs provide thepanelist and/or panelist household with a meter (e.g., a people meter)that monitors media presentation devices of the corresponding panelisthousehold. A people meter is an electronic device that is typicallypositioned in a media access area (e.g., an exposure area such as aliving room of the panelist household) and is proximate to and/orcarried by one or more panelists.

In some examples, the cost of selecting, monitoring, and analyzingenough panelists to produce a sufficiently representative subsection ofa region (e.g., a city, a county, etc.) may be substantial. As a result,the costs incurred to monitor panelists of small regions (e.g., lowpopulation-density regions, small cities, etc.) may be prohibitivelyexpensive for an AME to produce media exposure and/or demographics datafor such regions. Accordingly, AMEs often elect to enlist and monitorpanelists and, thus, collect media exposure and/or demographics data foronly the largest and/or most densely-populated regions.

Further, some households which are otherwise desirable for AMEs mayelect not to be a panelist household. For example, some householdmembers do not want to interact with a people meter before being exposedto media. For example, based on one or more triggers (e.g., a channelchange of a media presentation device or an elapsed period of time),some people meters generate a prompt for panelists to provide presenceand/or identity information by depressing a button of the people meter.Although periodically inputting information in response to a prompt maynot be burdensome when required for a short period of time, some peoplefind the prompting and data input tasks to be intrusive and annoyingover longer periods of time.

Because collecting information from panelists can be difficult andcostly, AMEs and other entities interested in measuring media/audienceshave begun to collect information from people and/or households that arenot traditional panelists via other sources such as data collected byset-top boxes and/or over-the-top devices (e.g., a Roku media device, anApple TV media device, a Samsung TV media device, a Google TV mediadevice, a Chromecast media device, an Amazon TV media device, a gamingconsole, a smart TV, a smart DVD player, an audio-streaming device,etc.). A set-top box (STB) is a device that converts source signals intomedia presented via a media presentation device. In some examples, theSTB implements a digital video recorder (DVR) and/or a digital versatiledisc (DVD) player. Some media presentation devices such as televisions,STBs and over-the-top devices are capable of recording tuning data formedia presentation.

As used herein, “tuning data” refers to information pertaining to tuningevents (e.g., a STB being turned on or off, channel changes, volumechanges, tuning duration times, etc.) of a media presentation device ofa household that is not associated with demographics data (e.g., numberof household members, age, gender, race, etc.) of the household and/ormembers of the household. To collect the tuning data of a mediapresentation device, consent is often obtained from the householdmembers for such data acquisition (e.g., via a third-party mediaprovider and/or manufacturer, the AME, etc.). Many people are willing toprovide tuning data via a media presentation device, becausepersonalized information is not collected by the media presentationdevice and repeated actions are not required of the household members.As used herein, people that consent to collection of tuning data (e.g.,via a media presentation device), but do not consent (and/or are notasked to consent) to collection of exposure data (e.g., media exposuredata that is tied to a particular person such as a panelist) and/ordemographics data, are referred to as “non-panelists.” While collectingtuning data from non-panelists can greatly increase the amount collecteddata about media presentation and/or exposure, the lack of exposure dataand/or demographic data reduces the value of this collected data.

To increase the value of tuning data collected from non-panelists inmeasuring the composition and size of audiences exposed to media in aregion, methods and apparatus disclosed herein enable AMEs (or any otherentity) to utilize minimum cross entropy to determine granular data of amedia audience of a region of interest (e.g., a target region, a regionof non-panelists) based on aggregate behavioral data (e.g., aggregatetuning data) of the region of interest, aggregate demographics data ofthe region of interest, and granular data of another region (e.g., asource region, a region of panelists).

As used herein, a “region of panelists,” a “panelist region,” and a“source region” refer to a geographic region (e.g., a neighborhood, atownship, a city, a county, etc.) that includes panelists from whichdata (e.g., demographics data, behavioral data) is collected to estimategranular data of that region. An example panelist region is a city(e.g., Chicago, Ill.) that includes panelists from which an AME and/orother entity collects demographic data (e.g., age, gender, income,highest-level education, political affiliation) and behavioral data(e.g., tuning data, viewing data, online activity data, purchasing data,etc.) to estimate the granular data for the city.

As used herein, “granular data,” “granular demographics,” and “granulardemographics data” refer to demographics data and behavioral data of aregion (e.g., a panelist region, a non-panelist region) that indicate arelationship between demographic constraints of the demographics dataand behavioral constraints of the behavioral data of the region. Forexample, granular data identifies a count or percentage of members ofthe region satisfying a demographic constraint of interest that alsosatisfy a behavioral constraint of interest. For example, granular datamay indicate that a region's audience for the show “Mike & Molly” (i.e.,a behavioral constraint) includes 10% of members of a region satisfyinga “young female” demographic constraint, 25% of members of the regionsatisfying an “old female” demographic constraint, 15% of members of aregion satisfying a “young male” demographic constraint, and 30% ofmembers of the region satisfying an “old male” demographic constraintviewed.

As used herein, a “region of non-panelists,” a “non-panelist region,”and a “target region” refer to a geographic region (e.g., aneighborhood, a township, a city, a county, etc.) that includesnon-panelists from which non-person-specific aggregate data (e.g.,aggregate behavioral data, aggregate demographics data) is collected. Anexample non-panelist region is a city (e.g., Rockford, Ill.) thatincludes non-panelists from which an AME and/or other entity collectsaggregate demographic data (e.g., age, gender, income, highest-leveleducation, political affiliation) and aggregate behavioral data (e.g.,tuning data, viewing data, online activity data, purchasing data, etc.)of the region.

As used herein, “aggregate behavioral data” refers tonon-person-specific data of a region (e.g., a non-panelist region) thatindicates a count and/or percentage of members of the region satisfyingbehavioral constraint(s) of interest. Example aggregate behavioral dataof a region includes aggregate tuning data collected from set-top boxesand/or over-the-top devices of households within the region that areassociated with tuning events of a corresponding media presentationdevice, the set-top box (e.g., turning a set-top box on or off, changinga channel, changing a volume), the over-the-top device, etc.

As used herein, a “behavioral constraint” refers to an event of interest(e.g., a tuning event, an exposure event) associated with a member(e.g., a panelist, a non-panelist) and/or a group of members of a region(e.g., a panelist region, a non-panelist region). An example behavioralconstraint includes media events tuned or exposed to members of aregion. For example, behavioral constraints include tuning to and/orviewing a channel (e.g., CBS) and/or a program (e.g., Mike & Molly) at aparticular time (7:30 P.M. on Monday).

As used herein, “aggregate demographics data” and “aggregatedemographics” refer to non-person-specific data of a region (e.g., anon-panelist region) that indicates a count and/or percentage of membersof the region that satisfy demographic constraint(s) of interest. Theaggregate demographics data of a region may be collected via asurvey-based census (e.g. a government-funded census, a privately-fundedcensus) of the region.

Example methods and apparatus disclosed herein utilize minimum crossentropy to determine granular data of a media audience of a non-panelistregion based on aggregate demographics data and aggregate behavioraldata of the non-panelist region and granular data of a panelist region.For example, an AME (or any other entity) obtains aggregate demographicsdata of the non-panelist region that indicates a count or percentage ofmembers of the non-panelist region that satisfy demographic constraintsof interest (e.g., a “young female” demographic constraint, an “oldmale” demographic constraint, etc.). Further, the example AME obtainsaggregate behavioral data of the non-panelist region that indicates acount or percentage of members of the non-panelist region that satisfybehavioral constraints of interest (e.g., a behavioral constraint forthe show “Good Times”, a behavioral constraint for the show “ER”, etc.).Further, the example AME obtains granular data of the panelist regionthat indicates a count or percentage of panelists satisfying thedemographic constraints of interest that also satisfy the behavioralconstraints of interest (e.g., a percentage of panelists satisfying the“old male” demographic constraint that also satisfy the behavioralconstraint for the show “Good Times,” a percentage of panelistssatisfying the “young female” demographic constraint that also satisfythe behavioral constraint for the show “ER,” etc.).

Based on the obtained data of the non-panelist region and the panelistregion, the example AME utilizes the minimum cross entropy to determinethe granular data of the media audience of the non-panelist region. TheAME utilizes the minimum cross entropy to enable multiple probabilitydistributions (e.g., aggregate demographics data, aggregate behavioraldata, granular demographics and behavioral data, etc.) that relate tooverlapping sets of events or characteristics (e.g., shared demographicsand/or behavioral constraints) to be compared. For example, by utilizingthe minimum cross entropy, the example AME is able to determine anestimate of the granular data of the media audience of the non-panelistregion even if there are non-linear relationships between the obtainedaggregate data of the non-panelist region and the obtained granular dataof the panelist region. In some examples, the AME determines whether todetermine the granular data of the media audience of the target regionvia the minimum cross entropy by evaluating the obtained granular dataof the panelist region. For example, the AME may analyze the samplesize, the margin of error, and/or other factors that indicate a highdegree of confidence of the obtained granular data of the panelistregion to determine whether to utilize the minimum cross entropy todetermine the granular data of the non-panelist region.

By utilizing the minimum cross entropy, the example AME calculates acount or percentage of the non-panelist region members satisfying thedemographic constraints of interest that also satisfy the behavioralconstraints of interest. As a result, the example methods and apparatusdisclosed herein enable AMEs and/or other entities to estimate granulardata for a region in which, for example, no panelists are employed byutilizing census data and tuning data associated with that region. Thus,the example methods and apparatus enable an AME and/or other entity toobtain granular data of a region that may be used to produce audiencemeasurement ratings for that region without having to enlist and monitorpanelists within that region. Accordingly, by obtaining granular data ofregions while reducing a number of regions in which panelists areenlisted and monitored, the example methods and apparatus disclosedherein reduce processing resources utilized by computer networked datacollection systems to meter regions and/or to transmit collected data ofthe metered regions. While the example methods and apparatus mayfacilitate estimation of regions in which panelists are not employed, afew or many panelists may optionally be employed in regions in whichestimates are computed.

Additionally or alternatively, the example methods and apparatusdisclosed herein may be used with the Online Campaign Ratings (OCR)systems and/or Digital Ad Rating (DAR) systems developed by The NielsenCompany (US), LLC to monitor online activity. Example OCR and DARsystems employ a technique disclosed in Blumenau, U.S. Pat. No.6,108,637, in which media distributed via a computer network (e.g., theInternet) is tagged with monitoring instructions (e.g., also known asbeacon instructions). In particular, monitoring instructions areassociated with the Hypertext Markup Language (HTML) of the media to betracked. When a client (e.g., a media presentation device) requests themedia, both the media and the beacon instructions are downloaded to theclient. The beacon instructions are, thus, executed whenever the mediais accessed, be it from a server or from a cache. The beaconinstructions cause monitoring data reflecting information about theaccess to the media to be sent from the client that downloaded the mediato a monitoring entity. Typically, the monitoring entity is an AME thatdid not provide the media to the client and who is a trusted third partyfor providing accurate usage statistics (e.g., The Nielsen Company,LLC). Because the beaconing instructions are associated with the mediaand executed by the client browser whenever the media is accessed, themonitoring information is provided to the AME irrespective of whetherthe client is a panelist of the AME.

In such examples involving OCR and/or DAR systems, the methods andapparatus disclosed herein enable an AME to utilize minimum crossentropy to determine granular data of a media audience of a region foractivities (e.g., impressions of online activity) conducted by regionmembers via a computer network system (e.g., the Internet) and monitoredby an AME or other entity via a computer networked data collectionsystem. Example methods and apparatus disclosed herein utilize theminimum cross entropy to determine the granular data of the mediaaudience of the region (e.g., a scaling value or weight for regionmembers satisfying a demographic constraint) based on aggregatebehavioral data of the region (e.g., a total count of online impressionsrecorded by the computer networked data collection system), aggregatedemographics data of the region (e.g., a count of region memberssatisfying the demographic constraint that have their online impressionrecorded for the demographic constraint by the computer networked datacollection system), and granular data of a sub-population of panelistsof the region (e.g., a scaling value or weight for panelists satisfyingthe demographic constraint).

Further, the example methods and apparatus disclosed herein relate tosubject matter disclosed in U.S. patent application Ser. No. 14/921,921,entitled “Methods and Apparatus to Calculate Granular Data of a RegionBased on Another Region for Media Audience Measurement” and filed onOct. 23, 2015, which is incorporated herein by reference in itsentirety.

Disclosed example methods for calculating granular data of a region formedia audience measurement include determining, by executing firstinstructions via a processor, aggregate behavioral data associated witha measurement of a media audience of a target region. The aggregatebehavioral data includes a first count of target region audience memberssatisfying a behavioral constraint. The example methods includedetermining, by executing second instructions via the processor,aggregate demographics data of the target region. The aggregatedemographics data includes a second count of the target region audiencemembers satisfying a first demographic constraint and a third count ofthe target region audience members satisfying a second demographicconstraint. The example methods include determining, by executing thirdinstructions via the processor, granular data of a source region. Thegranular data includes a fourth count of source region audience memberssatisfying the behavioral constraint and the first demographicconstraint and a fifth count of the source region audience memberssatisfying the behavioral constraint and the second demographicconstraint. The example methods include calculating, by executing fourthinstructions via the processor, granular data of the media audience ofthe target region by utilizing minimum cross entropy to apportion theaggregate demographics data and the aggregate behavioral data of thetarget region based on the granular data of the source region. Thegranular data of the media audience of the target region includes asixth count of the target region audience members satisfying thebehavioral constraint and the first demographic constraint and a seventhcount of the target region audience members satisfying the behavioralconstraint and the second demographic constraint.

In some example methods, the first demographic constraint and the seconddemographic constraint are mutually exclusive.

In some example methods, utilizing the minimum cross entropy tocalculate the granular data of the media audience of the target regionincludes performing non-linear optimization based on the granular dataof the source region, the aggregate demographics data of the targetregion, and the aggregate behavioral data of the target region. In somesuch example methods, utilizing the minimum cross entropy to calculatethe granular data of the media audience of the target region includesdefining an optimization constraint based on the aggregate behavioraldata and the aggregate demographics data of the target region. Thenon-linear optimization is limited by the optimization constraint. Somesuch example methods include, prior to utilizing the minimum crossentropy, determining whether to calculate the granular data of the mediaaudience of the target region via the minimum cross entropy byevaluating the fourth count and the fifth count of the granular data ofthe source region.

In some example methods, determining the aggregate behavioral data ofthe target region includes determining tuning data of the target regionand determining the granular data of the source region includesdetermining exposure data of the source region. The target region is anon-panelist region and the source region is a panelist region. Thenon-panelist region and the panelist region are mutually exclusive.

In some example methods, determining the aggregate behavioral data ofthe target region includes determining impressions data of thepopulation and determining the granular data of the source regionincludes determining impressions data associated with demographics dataof the panelists. The target region is a population and the sourceregion is a sub-region of panelists of the population.

In some example methods, determining the granular data of the targetregion based on the aggregate demographics data of the target region,the aggregate behavioral data of the target region, and the granulardata of the source region reduces an amount of data collected bycomputer networked data collection systems to determine the granulardata of the target region by calculating the granular data of the targetregion without collecting the granular data from the target region.

In some example methods, the processor includes at least a firstprocessor of a first hardware computer system and a second processor ofa second hardware computer system.

Disclosed example apparatus for calculating granular data of a regionfor media audience measurement include a target region determiner todetermine aggregate behavioral data associated with a measurement of amedia audience of a target region. The aggregate behavioral dataincludes a first count of target region audience members satisfying abehavioral constraint. The target region determiner is to determineaggregate demographics data of the target region. The aggregatedemographics data includes a second count of the target region audiencemembers satisfying a first demographic constraint and a third count ofthe target region audience members satisfying a second demographicconstraint. The example apparatus include a source region determiner todetermine granular data of a source region. The granular data includes afourth count of source region audience members satisfying the behavioralconstraint and the first demographic constraint and a fifth count of thesource region audience members satisfying the behavioral constraint andthe second demographic constraint. The example apparatus include atarget region calculator to calculate granular data of the mediaaudience of the target region by utilizing minimum cross entropy toapportion the aggregate demographics data and the aggregate behavioraldata of the target region based on the granular data of the sourceregion. The granular data of the media audience of the target regionincludes a sixth count of the target region audience members satisfyingthe behavioral constraint and the first demographic constraint and aseventh count of the target region audience members satisfying thebehavioral constraint and the second demographic constraint.

In some example apparatus, the first demographic constraint and thesecond demographic constraint are mutually exclusive.

In some example apparatus, the target region calculator utilizes theminimum cross entropy to determine the granular data of the mediaaudience of the target region by performing non-linear optimizationbased on the granular data of the source region, the aggregatedemographics data of the target region, and the aggregate behavioraldata of the target region. In some such examples, the target regioncalculator is to utilize the minimum cross entropy to determine thegranular data of the media audience of the target region by defining anoptimization constraint based on the aggregate behavioral data and theaggregate demographics data of the target region. The non-linearoptimization is limited by the optimization constraint. Some suchexample apparatus include that, prior to the target region calculatorutilizing the minimum cross entropy, the target region calculator is todetermine whether to determine the granular data of the media audienceof the target region by evaluating the fourth count and the fifth countof the granular data of the source region.

In some example apparatus, the target region determiner is to determinetuning data of the target region to determine the aggregate behavioraldata of the target region and is to determine exposure data of thesource region to determine the granular data of the source region. Thetarget region is a non-panelist region and the source region is apanelist region. The non-panelist region and the panelist region aremutually exclusive.

In some example apparatus, the target region determiner is to determineimpressions data of the population to determine the aggregate behavioraldata of the target region and is to determine impressions dataassociated with demographics data of the panelists to determine thegranular data of the source region. The target region is a populationand the source region is a sub-region of panelists of the population.

FIG. 1 is a block diagram of an example environment 100 that includes atarget region 102, a source region 104, an AME 106, and a network 108.In the illustrated example, the target region 102 (e.g., a non-panelistregion) includes households 110 a, 110 b (e.g., non-panelisthouseholds), and the source region 104 (e.g., a panelist region)includes households 112 a, 112 b (e.g., panelist households). Asdiscussed in further detail below, the AME 106 of the exampleenvironment 100 calculates and/or estimates granular data of the targetregion 102 (e.g., to produce media ratings of the target region 102)based on aggregate demographics data and aggregate behavioral data ofthe target region 102 and granular data of the source region 104.Further, as discussed below, the network 108 of the illustrated example,among other things, communicatively couples the AME 106 to thehouseholds 110 a, 110 b, 112 a, 112 b of the respective first and sourceregions 102, 104.

The households 110 a, 110 b (e.g., non-panelist households) of thetarget region 102 (e.g., a non-panelist region) include respectivemembers 114 a, 114 b, 114 c (e.g., non-panelists), media presentationdevices 116 a, 116 b, and STBs 118 a, 118 b. For example, the household110 a includes the members 114 a, 114 b, the media presentation device116 a, and the STB 118 a, and the household 110 b includes the member114 c, the media presentation device 116 b, and the STB 118 b.

In some examples, the households 110 a, 110 b are representative of manyother households (e.g., other non-panelist households) that may beincluded in the example target region 102. Characteristics of the otherhouseholds (e.g., a number of household members, demographics of thehousehold members, a number of televisions, etc.) may be similar toand/or different from those of the representative households 110 a, 110b. For example, other households include one member, two members, threemembers, four members, etc.

The STBs 118 a, 118 b of the illustrated example convert source signalsinto media that are presented via the respective media presentationdevices 116 a, 116 b. In some examples, the STBs 118 a, 118 b implementa digital video recorder (DVR) and/or a digital versatile disc (DVD)player. In the illustrated example, the STBs 118 a, 118 b are incommunication with the respective media presentation device 116 a, 116 bvia wireless connections (e.g., Bluetooth, Wi-Fi, etc.) or via wiredconnections (e.g., Universal Serial Bus (USB), etc.) to transmitconverted source signals from the STBs 118 a, 118 b to the respectivemedia presentation devices 116 a, 116 b. In some examples, the STBs 118a, 118 b are integrated into the respective media presentation devices116 a, 116 b. In the illustrated example, the media presentation devices116 a, 116 b are televisions. In alternative examples, the mediapresentation devices 116 a, 116 b are computers (e.g., desktopcomputers, laptop computers, etc.), speakers, stereos, portable devices(e.g., tablets, smartphones, etc.), gaming consoles (e.g., Xbox Ones®,Playstation® 4s, etc.), online media presentation devices (e.g., GoogleChromecasts, Rokus® Streaming Sticks®, Apple TVs®, etc.) and/or anyother type of media presentation devices.

As illustrated in FIG. 1, the tuning data 120 a, 120 b (e.g., behavioraldata) and demographics data 122 a, 122 b are collected from therespective households 110 a, 110 b of the target region 102. The tuningdata 120 a, 120 b collected by the example STBs 118 a, 118 b areassociated with tuning events of the STBs 118 a, 118 b and/or therespective media presentation devices 116 a, 116 b (e.g., turning theSTBs 118 a, 118 b on or off, changing channels presented via the mediapresentation devices 116 a, 116 b, increasing or lowering the volume,remaining on a channel for a duration of time, etc.) to monitor media(e.g., television programming, radio programming, movies, songs,advertisements, Internet-based programming such as websites and/orstreaming media, etc.) presented by the respective media presentationdevices 116 a, 116 b. For example, the tuning events of the tuning data120 a, 120 b are identified by channel (e.g., CBS, ABC, Fox, TV Land,TBS, FXX, etc.) and time (e.g., a particular time such as 7:10 A.M. or8:31 P.M., a predetermined time-period segment such as 7:00-7:15 A.M. or8:00-8:30 P.M., etc.).

The tuning data 120 a, 120 b collected and/or recorded by the respectiveSTBs 118 a, 118 b do not include exposure data (e.g., data indicatingwhich members are exposed to particular media) or demographics data(e.g., data indicating a number of household members, age, gender, race,etc.) of the respective households 110 a, 110 b. For example, if thehousehold member 114 b is viewing the show “Roseanne” via the mediapresentation device 116 a, the tuning data 120 a recorded by the STB 118a indicates that the STB 118 a was tuned to TV Land at 6:00 A.M. onFriday, but does not identify that the household member 114 b wasexposed to the show “Roseanne” or include demographics data of thehousehold member 114 b.

The example demographics data 122 a, 122 b include information regardingdemographic constraints (e.g., demographic marginals of respectivedemographic dimensions, combinations of demographic marginals ofcombinations of respective demographic dimensions, etc.) of the targetregion 102, but do not include member-specific information of themembers 114 a, 114 b, 114 c or household-specific information of thehouseholds 110 a, 110 b of the target region 102. That is, the exampledemographics data 122 a, 122 b do not indicate which members 114 a, 114b, 114 c or households 110 a, 110 b of the target region 102 areassociated with demographics of the collected demographics data 122 a,122 b. In the illustrated example, the demographics data 122 a, 122 bassociated with the households 110 a, 110 b of the target region 102 arecollected via a survey-based census (e.g. a government-funded census, aprivately-funded census).

As illustrated in FIG. 1, the households 112 a, 112 b (e.g., panelisthouseholds) of the source region 104 (e.g., a panelist region) includerespective members 124 a, 124 b, 124 c (e.g., panelists), mediapresentation devices 126 a, 126 b, and meters 128 a, 128 b (e.g., peoplemeters). For example, the household 112 a includes the members 124 a,124 b, the media presentation device 126 a, and the meter 128 a, and thehousehold 112 b includes the member 124 c, the media presentation device126 b, and the meter 128 b.

In some examples, the households 112 a, 112 b are representative of manyother households (e.g., other panelist households) that may be includedin the example source region 104. Characteristics of the otherhouseholds (e.g., a number of household members, demographics of thehousehold members, a number of televisions, etc.) may be similar toand/or different from those of the representative households 112 a, 112b. For example, other households include one member, two members, threemembers, four members, etc.

The meters 128 a, 128 b of the illustrated example are electronicdevices that are positioned in media access areas (e.g., exposure areassuch as living rooms of the households 112 a, 112 b) proximate to therespective media presentation devices 126 a, 126 b to monitor the mediapresented via the respective media presentation devices 126 a, 126 band/or the media exposed to the members 124 a, 124 b, 124 c. That is,the example meters 128 a, 128 b of the source region 104 collectexposure data 130 a, 130 b, 130 c that identifies whether thecorresponding members 124 a, 124 b, 124 c were exposed to displayedmedia, while the STBs 118 a, 118 b of the target region 102 collect thetuning data 120 a, 120 b that identifies tuning events of the STBs 118a, 118 b and/or the media presentation devices 116 a, 116 b but do notidentify whether a member is exposed to the tuned event). Additionallyor alternatively, the example panelists 124 a, 124 b, 124 c may carrycorresponding personal people meters (e.g., electronic devicesdesignated to the members 124 a, 124 b, 124 c) that monitor the mediaexposed to those corresponding members 124 a, 124 b, 124 c.

In the illustrated example, the media presentation devices 126 a, 126 bare televisions. In alternative examples, the media presentation devices126 a, 126 b are computers (e.g., desktop computers, laptop computers,etc.), speakers, stereos, portable devices (e.g., tablets, smartphones,etc.), gaming consoles (e.g., Xbox Ones®, Playstation® 4s, etc.), onlinemedia presentation devices (e.g., Google Chromecasts, Rokus® StreamingSticks®, Apple TVs®, etc.) and/or any other type of media presentationdevices.

As illustrated in FIG. 1, the example exposure data 130 a, 130 b, 130 c(e.g., behavioral data) and demographics data 132 a, 132 b, 132 c arecollected from the respective households 112 a, 112 b of the sourceregion 104. The example exposure data 130 a, 130 b, 130 c are associatedwith media events exposed (e.g., exposure events) to the respectivemembers 124 a, 124 b, 124 c of the source region 104. The exampleexposure data 130 a, 130 b, 130 c identify programs (e.g., FamilyMatters, Chicago PD, Sirens, According to Jim, The League, etc.),channels (CBS, NBC, ABC, TV Land, USA Network, FXX, etc.), and/or times(e.g., particular times such as 7:10 A.M. or 8:31 P.M., predeterminedtime-period segments such as 7:00-7:15 A.M. or 8:00-8:30 P.M., etc.)associated with the exposure events. The example exposure data 130 a,130 b, 130 c identify which member(s) (e.g., the example members 124 a,124 b, 124 c) are associated with the exposure events. Further, theexample exposure data 130 a, 130 b, 130 c may be associated with thecorresponding demographics data (e.g., the demographics data 132 a, 132b, 132 c) of the identified members. As an example, if the member 124 ais exposed to the show “Married . . . With Children,” the exposure data130 a identifies the program (i.e., the show “Married . . . WithChildren”), the channel (TBS), the time (e.g., 8:30 A.M. on Thursday)and/or the member (i.e., the member 124 a) associated with the exposureevent and is associated with the corresponding demographics data (e.g.,the demographics data 130 a) of the member.

In the illustrated example, the demographics data 132 a includesperson-specific information associated with the member 124 a, thedemographics data 132 b includes person-specific information associatedwith the member 124 b, and the demographics data 132 c includesperson-specific information associated with the member 124 c. Thedemographics data 132 a, 132 b, 132 c of the illustrated exampleidentify which demographic constraints (e.g., demographic marginals ofrespective demographic dimensions, combinations of demographic marginalsof combinations of respective demographic dimensions, etc.) areassociated with the corresponding members 124 a, 124 b, 124 c of thesource region 104. For example, the demographics data 132 a indicatethat the member 124 a satisfies the “white, middle-aged, male”demographic constraint, the demographics data 132 b indicate that themember 124 b satisfies the “black, middle-aged, female” demographicconstraint, and the demographics data 132 c indicate that the member 124c satisfies the “Latino, young, female” demographic constraint. Thedemographics data 132 a, 132 b, 132 c may be provided by the members 124a, 124 b, 124 c via, for example, self-reporting, responding to surveys,providing consent for entities (e.g., AMEs) to obtain such informationfrom database proprietors (e.g., Facebook, Twitter, Google, Yahoo!, MSN,Apple, Experian, etc.), etc. In some examples, the demographics data 132a, 132 b, 132 c are collected from the members 124 a, 124 b, 124 c uponand/or after the members 124 a, 124 b, 124 c are enlisted as panelists.

From time to time (periodically, aperiodically, randomly, when datacapacity is reached, etc.), the STBs 118 a, 118 b communicate thecollected tuning data 120 a, 120 b of the target region 102 and themeters 128 a, 128 b communicate the collected exposure data 130 a, 130b, 130 c of the source region 104 to the AME 106 via the network 108(e.g., the Internet, a local area network, a wide area network, acellular network, etc.) via wired and/or wireless connections (e.g., acable/DSL/satellite modem, a cell tower, etc.).

The AME 106 of the illustrated example utilizes the collecteddemographics data 122 a, 122 b and the collected tuning data 120 a, 120b of the target region 102 (e.g., a non-panelist region) and thecollected demographics data 132 a, 132 b, 132 c and the collectedexposure data 130 a, 130 b, 130 c of the source region 104 (e.g., apanelist region) to utilize minimum cross entropy to determine granulardata of the target region 102. In the illustrated example, the exampleAME 106 (e.g., The Nielsen Company (US), LLC®) utilizes the minimumcross entropy to calculate the granular data of the target region 102 toproduce media ratings (e.g., a composition and/or size of a mediaaudience) for the target region. The ratings produced by the example AME106 may be used by advertisers and/or marketers to purchase advertisingspace and/or design advertising campaigns. Additionally oralternatively, the ratings produced by the example AME 106 are used bymedia producers and/or distributors to determine how to set prices foradvertising space and/or make programming decisions.

As illustrated in FIG. 1, the AME 106 includes a target regiondemographics database 134, a target region behavioral database 136, asource region database 138, and a demographics estimator 140.

The target region demographics database 134 of the illustrated examplestores the demographics data (e.g., the demographics data 122 a, 122 b)of the target region 102 in a non-person-specific,non-household-specific aggregate form. That is, the example targetregion demographics database 134 stores aggregate demographics data ofthe target region 102 that indicates count(s) and/or percentage(s) ofmembers of the target region 102 satisfying demographic constraint(s) ofinterest (e.g., a “young female” demographic constraint, an “old female”demographic constraint, a “young male” demographic constraint, an “oldmale” demographic constraint, etc.) without identifying which members(e.g., the members 114 a, 114 b, 114 c) and/or households (e.g., thehouseholds 110 a, 110 b) are associated with those demographicconstraints.

The target region behavioral database 136 of the illustrated examplestores the behavioral data (e.g., the tuning data 120 a, 120 b) of thetarget region 102 in a non-person-specific, non-household-specificaggregate form. That is, the example target region behavioral database134 stores aggregate behavioral data of the target region 102 thatindicates count(s) and/or percentage(s) of members of the target region102 satisfying behavioral constraint(s) of interest (a behavioralconstraint for the show “Shameless,” a behavioral constraint for theshow “Chicago Fire,” a behavioral constraint for the show “The GoodWife,” etc.) without identifying which members (e.g., the members 114 a,114 b, 114 c) and/or households (e.g., the households 110 a, 110 b) areassociated with those behavioral constraints.

The source region database 138 of the illustrated example stores thedemographics data (e.g., the demographics data 132 a, 132 b, 132 c) andthe behavioral data (e.g., the exposure data 130 a, 130 b, 130 c) of thesource region 104 in granular form. That is, the example source regiondatabase 138 stores granular data of the source region 104 thatindicates count(s) and/or percentage(s) of members of the target region104 satisfying behavioral constraint(s) of interest (a behavioralconstraint for the show “Shameless,” a behavioral constraint for theshow “Chicago Fire,” a behavioral constraint for the show “The GoodWife,” etc.) that also satisfy demographic constraint(s) of interest(e.g., a “young female” demographic constraint, an “old female”demographic constraint, a “young male” demographic constraint, an “oldmale” demographic constraint, etc.).

Based on the aggregate demographics data of the target regiondemographics database 134, the aggregate behavioral data of the targetregion behavioral database 136, and the granular data of source regiondatabase 138, the demographics estimator 140 of the illustrated exampleperforms non-linear optimization to utilize minimum cross entropy todetermine granular data of the target region 102. For example, based onaggregate data of the target region 102 and granular data of the sourceregion 104 (e.g., a panelist region), the demographics estimator 140utilizes the minimum cross entropy to calculate granular data of thetarget region 102 (e.g., a non-panelist region) to measure a size and/orcomposition of media audiences in the target region 102.

In operation, non-person-specific demographics data (e.g., thedemographics data 122 a, 122 b) and non-person-specific behavioral data(e.g., the tuning data 120 a, 120 b) are collected from households(e.g., the households 110 a, 110 b) of a non-panelist region (e.g., thetarget region 102). Further, person-specific demographics data (e.g.,the demographics data 132 a, 132 b, 132 c) and person-specificbehavioral data (e.g., the exposure data 130 a, 130 b, 130 c) arecollected from households (e.g., the households 112 a, 112 b) of apanelist region (e.g., the source region 104). The collecteddemographics and behavioral data are sent to the AME 106 via the network108. The target region demographics database 134 of the AME 106 storesthe demographics data of the non-panelist region in aggregate form, thetarget region behavioral database 136 stores the behavioral data of thenon-panelist region in aggregate form, and the source region database138 stores the demographics and behavioral data of the panelist region104 in granular form. Based on the aggregate data of the non-panelistregion and the granular data of the panelist region, the demographicsestimator 140 utilizes the minimum cross entropy to determine granulardata of the non-panelist region that may be used to measure mediaaudiences of the non-panelist region.

The example methods and apparatus disclosed herein utilize minimum crossentropy to determine granular data of a target region based on aggregatedata of the target region and granular data of another region (e.g., asource region) to, for example, address the technological problem ofreducing an amount of data that is collected from the target region bycomputer networked data collection systems to determine the granulardata of the target region. Further, by utilizing the minimum crossentropy to calculate the granular data of the target region based on, inpart, the aggregate data of the target region, the disclosed examplemethods and apparatus provide a solution to the technological problem ofdetermining the granular data of the target region based onnon-person-specific aggregate tuning data (e.g., tuning data notassociated with demographics data) that is collected from the targetregion by computer networked data collection systems.

FIG. 2 is a block diagram of an example implementation of the exampledemographics estimator 140 of FIG. 1 that is to utilize minimum crossentropy to determine granular data of the example target region 102 ofFIG. 1. As illustrated in FIG. 2, the example demographics estimator 140includes an example target region determiner 202, an example sourceregion determiner 204, and an example target region calculator 206.

The target region determiner 202 of the illustrated example determinesaggregate demographics data 208 of the example target region 102. Forexample, the target region determiner 202 collects the aggregatedemographics data 208 that is based on the example demographics data 122a, 122 b of the example households 110 a, 110 b (e.g., non-panelisthouseholds) of the target region 102 (e.g., a non-panelist region) fromthe example target region demographics database 134 of FIG. 1. Forexample, the aggregate demographics data 208 collected by the targetregion determiner 202 includes non-person-specific andnon-household-specific data collected via a survey-based census (e.g. agovernment-funded census, a privately-funded census). In some examples,the target region determiner 202 obtains the aggregate demographics data208 from the target region demographics database 134 via a network(e.g., the Internet, a local area network, a wide area network, acellular network, etc.) and wired and/or wireless connections (e.g., acable/DSL/satellite modem, a cell tower, etc.).

As illustrated in FIG. 2, the example target region determiner 202collects the example aggregate demographics data 208 in vector form.Elements of the example aggregate demographics data 208 correspond todemographic constraints of interest. For example, an element of a firstrow of the example aggregate demographics data 208 corresponds with a“young female” demographic constraint, an element of a second rowcorresponds with an “old female” demographic constraint, an element of athird row corresponds with a “young male” demographic constraint, and anelement of a fourth row corresponds with an “old male” demographicconstraint. Additionally or alternatively, the example aggregatedemographics data 208 may include elements that correspond todemographic constraints associated with other demographic marginals(e.g., income, race, nationality, geographic location, education level,religion, etc.), demographic joint-marginals (e.g., a gender/race/incomedemographic joint-marginal), demographic joints (e.g., agender/race/income/education-level demographic joint), and/or anycombination thereof.

The elements of the example aggregate demographics data 208 representquantities (e.g., counts, percentages) of the target region 102 thatmatch, belong to and/or satisfy the corresponding demographics ofinterest. As illustrated in FIG. 2, the elements of the exampleaggregate demographics data 208 are normalized to a value of ‘1.0’ suchthat the sum of the elements of the aggregate demographics data 208equals a value of ‘1.0.’ For example, the element of the first row ofthe example aggregate demographics data 208 includes a value of ‘0.3’that indicates 30% of members of the target region 102 are youngfemales, the element of the second row includes a value of ‘0.1’ thatindicates 10% of members of the target region 102 are old females, theelement of the third row includes a value of ‘0.4’ that indicates 40% ofmembers of the target region 102 are young males, and the element of thefourth row includes a value of ‘0.2’ that indicates 20% of members ofthe target region 102 are old males.

Further, the example target region determiner 202 determines aggregatetuning data 210 (e.g., aggregate behavioral data) of the example targetregion 102. For example, the target region determiner 202 collects theaggregate tuning data 210 that is based on the examplenon-person-specific tuning data 120 a, 120 b of the example households110 a, 110 b (e.g., the non-panelist households) of the first region 102(e.g., the non-panelist region) from the example target regionbehavioral database 136 of FIG. 1. In some examples, the target regiondeterminer 202 obtains the aggregate tuning data 210 from the targetregion behavioral database 136 via a network (e.g., the Internet, alocal area network, a wide area network, a cellular network, etc.) andwired and/or wireless connections (e.g., a cable/DSL/satellite modem, acell tower, etc.).

As illustrated in FIG. 2, the example target region determiner 202collects the example aggregate tuning data 210 in vector form. Elementsof the example aggregate tuning data 210 correspond to behavioralconstraints (e.g., tuning events) of interest. For example, an elementof a first row of the example aggregate tuning data 210 corresponds witha behavioral constraint for the show “Shameless,” an element of a secondrow corresponds with a behavioral constraint for the show “ChicagoFire,” and an element of a third row corresponds with a behavioralconstraint for the show “The Good Wife.” Additionally or alternatively,the example aggregate demographics data 208 may include elements thatcorrespond to other behavioral constraints (e.g., tuning durations,channels tuned, tuning times, etc.). Alternatively, the behavioralconstraints of interest correspond to elements of columns of a vectorform of the aggregate tuning data 210.

The elements of the example aggregate tuning data 210 representquantities (e.g., counts, percentages, ratings points, ratings shares,etc.) of households of the target region 102 (e.g., the households 110a, 110 b) that match, belong to and/or satisfy the correspondingbehavioral characteristics (e.g., tuning events) of interest. Forexample, a value of ‘0.075’ in the first row of the example aggregatetuning data 212 indicates that 7.5% of the households of the targetregion 102 (e.g., the example households 110 a, 110 b of FIG. 1) tunedto a first program (e.g., “Shameless”), a value of ‘0.01’ in the secondrow indicates that 10% of the households were tuned to a second program(e.g., “Chicago Fire”), and a value of ‘0.035’ in the third rowindicates that 3.5% of the households were tuned to a third program(e.g., “The Good Wife”).

The source region determiner 204 of the illustrated example determinersgranular data 212 of the example source region 104 of FIG. 1. Forexample, the source region determiner 204 collects the granular data 212that is based on exposure data (e.g., the example exposure data 130 a,130 b, 130 c of FIG. 1) and demographics data (e.g., the exampledemographics data 132 a, 132 b, 132 c of FIG. 1) of panelist households(e.g., the example households 112 a, 112 b of FIG. 1) of the sourceregion 104 (e.g., a panelist region) from the example source regiondatabase 138 of FIG. 1. In some examples, the source region determiner204 obtains the granular data 212 from the source region database 138via a network (e.g., the Internet, a local area network, a wide areanetwork, a cellular network, etc.) and wired and/or wireless connections(e.g., a cable/DSL/satellite modem, a cell tower, etc.).

As illustrated in FIG. 2, the example source region determiner 204collects the example granular data 212 in matrix form. In theillustrated example, rows of the granular data 212 collected by thesource region determiner 204 correspond to behavioral constraints ofinterest, and columns of the granular data 212 correspond to demographicconstraints of interest. The behavioral constraints corresponding to therows of the example granular data 212 are the same behavioralconstraints of the example aggregate tuning data 210. For example, afirst row of the granular data 212 collected by the example sourceregion determiner 204 corresponds with a behavioral constraint for theshow “Shameless,” a second row corresponds with a behavioral constraintfor the show “Chicago Fire,” and a third row corresponds with abehavioral constraint for the show “The Good Wife.” Further, thedemographic constraints corresponding to the columns of the examplegranular data 212 are the same demographic constraints of the exampleaggregate demographics data 208. For example, a first column of thegranular data 212 collected by the example source region determiner 204corresponds with a “young female” demographic constraint, a secondcolumn corresponds with an “old female” demographic constraint, a thirdcolumn corresponds with a “young male” demographic constraint, and afourth column corresponds with an “old male” demographic constraint.

Elements of the granular data 212 collected by the source regiondeterminer 204 represent values indicative of quantities (e.g., counts,percentages, ratings points, ratings shares, etc.) of members of thesource region 104 matching, satisfying, and/or belonging to thecorresponding behavioral constraint that also match, satisfy, and/orbelong to the corresponding demographic constraint. For example, a valueof ‘0.08’ in the first row and the first column of the example granulardata 212 indicates that 8% of young females of the source region 104were exposed to the show “Shameless.” Similarly, a value of ‘0.04’ inthe first row and the second column indicates that 4% of old femaleswere exposed to the show “Shameless,” a value of ‘0.1’ in the first rowand the third column indicates that 10% of young males were exposed tothe show “Shameless,” and a value of ‘0.03’ in the first row and thefourth column indicates that 3% of old males were exposed to the show“Shameless.” Further, as illustrated in the example granular data 212 ofFIG. 2, a value of ‘0.15’ in the second row and the first columnindicates that 15% of young females were exposed to the show “ChicagoFire,” and a value of ‘0.01’ in the third row and the first columnindicates that 1% of young females were exposed to the show “The GoodWife.”

In the illustrated example, the target region calculator 206 utilizesminimum cross entropy to determine or calculate target region granulardata 214. The minimum cross entropy may be utilized to performnon-linear optimization on multiple probability distributions (e.g.,aggregate demographics data, aggregate behavioral data, granularaggregate and demographics data, etc.) that relate to overlapping setsof characteristics (e.g., shared behavioral characteristics and/ordemographic characteristics). For example, the target region calculator206 utilizes the minimum cross entropy to calculate the granular data214 of the target region 102 based on the aggregate demographics data208 and the aggregate behavioral data 210 of the target region 102 andthe granular data 212 of the source region 104. The target regiongranular data 214 calculated by the target region calculator 206 via theminimum cross entropy includes estimates of quantities (e.g., counts,percentages, ratings points, ratings shares, etc.) of members of thetarget region 102 matching, satisfying, and/or belonging to behavioralconstraints of interest that also match, satisfy, and/or belong todemographic constraints of interest.

The target region calculator 206 utilizes the minimum cross entropy tocalculate the target region granular data 214 to reduce variability ofsmall values (e.g. values near, close to and/or approximate ‘0.0’ or 0%)and/or large values (e.g., values near, close to and/or approximate‘1.00’ or 100%) of the target region granular data 214 relative to thosecorresponding values of the granular data 212 of the source region 104to increase accuracy and/or certainty of the calculated target regiongranular data 214. For example, the minimum cross entropy utilized bythe target region calculator 206 allows for a greater difference ofintermediate values (e.g., values away from ‘0.0’ or 0% and ‘1.00’ and100%, values more approximate to ‘0.50’ or 50% compared to the smalland/or large values) to reduce an amount of difference of the smallvalues of the target region granular data 214, because a difference of aparticular value (e.g., +/−‘0.02’ or 2%) increases a variability of asmall values (e.g., ‘0.03’ or 3%) more than it increases a variabilityof an intermediate value (e.g., ‘0.2’ or 20%). In some examples, becausethe minimum cross entropy reduces variability of small values of thetarget region granular data 214, the target region calculator 206determines whether to utilize the minimum cross entropy upon identifyingthat the target region granular data 214 is based on data (the aggregatedemographics data 208 of the target region 102, the aggregate behavioraldata 210 of the target region 102, and/or the granular data 212 of thesource region 104) in which there is a high degree of confidence (e.g.,as a result of a large sample size, a small margin of error, and/orother factors indicating confidence).

The example target region calculator 206 utilizes minimum cross entropyfor each of the behavioral constraints on interest to determine thegranular data 214 of the target region 102. For example, the targetregion calculator 206 utilizes minimum cross entropy for the constraintassociated with the show “Shameless,” again utilizes minimum crossentropy for the constraint associated with the show “Chicago Fire,” andagain utilizes minimum cross entropy for the constraint associated withthe show “The Good Wife.” The minimum cross entropies are utilized tocalculate the granular data 214 of the respective behavioral constraintsby minimizing Equation 1 provided below.

$\begin{matrix}{{D\left( {P\text{:}\mspace{14mu} Q} \right)} = {{- {\sum\limits_{i = 1}^{n}{p_{i}{\log \left( \frac{p_{i}}{q_{i}} \right)}}}} - {\sum\limits_{i = 1}^{n}{\left( {1 - p_{i}} \right){\log \left( \frac{1 - p_{i}}{1 - q_{i}} \right)}}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1 provided above, P represents the granular data 214 of thetarget region 102 to be determined, Q represents the granular data 212of the source region 104 determined by the source region determiner 204,p_(i) represents the target region granular data 214 of the behavioralconstraint i to be calculated (e.g., in decimal form such that p_(i)equals a value of ‘0.1’ when 10% of a population is tuned to aparticular program), and q_(i) represents the granular data 212 of thebehavioral constraint i (e.g., in decimal form such that q_(i) equals avalue of ‘0.075’ when 7.5% of a population is tuned to a particularprogram) that is determined by the source region determiner 204.

To enable the non-linear optimization to be performed via the minimumcross entropy, Equation 1 may be solved via a partial derivative of theLagrangian (e.g., Equation 1 is solved given that the right-hand side ofpartial the derivative of the Lagrangian of Equation 1 equals a value of‘0’). An example of the solution of Equation 1 is provided below inEquation 2.

$\begin{matrix}{\frac{p_{i}}{1 - p_{i}} = {\frac{q_{i}}{1 - q_{i}}e^{{- \lambda}\; w_{i}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In Equation 2 provided above, p_(i) represents the target regiongranular data 214 of the behavioral constraint i (e.g., in decimalform), q_(i) represents the granular data 212 of the behavioralconstraint i (e.g., in decimal form) that is determined by the sourceregion determiner 204, and w_(i) represents the aggregate demographicsdata 208 associated with the behavioral constraint i (e.g., in decimalform) determined by the target region determiner 202. For example, p₁represents a probability associated with a first behavioral constraint(e.g., tuning to the show “Shameless”) of the target region granulardata 214, p₂ represents a probability value associated with a secondbehavioral constraint (e.g., tuning to the show “Chicago Fire”) of thetarget region granular data 214, p₃ represents a probability associatedwith a third behavioral constraint (e.g., tuning to the show “The GoodWife”) value of the target region granular data 214, etc. The exampletarget region calculator 206 utilizes the minimum cross entropy via theabove-provided Equation 2 by solving for λ.

The relationships of equations 1 and 2 are constructed such that eachcalculated value, p_(i), is a positive value between ‘0.0’ and ‘1.0’(e.g., between 0% and 100% if written in percentage form). That is, therelationships of equations 1 and 2 are constructed such that 0≦p_(i)≦1.0for each value of the target region granular data 214.

Further, the example target region calculator 206 performs non-linearoptimization (e.g., utilizes minimum cross entropy) of theabove-provided Equation 1 subject to an equality constraint representedas Pw=C in which P represents the granular data 214 of the target region102 to be determined, w represents the aggregate demographics data 208of the target region 102 determined by the target region determiner 202,and C represents the aggregate tuning data 210 of the target region 102determined by the target region determiner 202. For example, P of theequality constraint includes p₁, p₂, p₃, and/or any other values of thetarget region granular data 214 (e.g., p_(i)) of Equation 2 providedabove. In some examples, the target region calculator 206 utilizesminimum cross entropy to calculate values that approach, are approximateto, and/or equal minimum cross entropy probabilities associated with thebehavioral constraints of the target region granular data 214. Thus, thetarget region calculator 206 utilizes minimum cross entropy to determinethe target region granular data 214 based on the example aggregatedemographics data 208 (e.g., w of the above-identified equalityconstraint and/or w_(i) of Equations 1 and 2), the example aggregatetuning data 210 (e.g., C of the above-identified equality constraint),and the example granular data 214 of the source region 104 (e.g., P ofthe above-identified equality constraint and/or p_(i) of Equations 1 and2).

In the illustrated example, the target region calculator 206 utilizesthe minimum cross entropy to calculate the target region granular data214 as shown in the example below in Table 1.

TABLE 1 Young Female Old Female Young Male Old Male Shameless 0.0810.0402 0.1016 0.0303 Chicago Fire 0.2736 0.0383 0.027 0.0165 The GoodWife 0.0126 0.0431 0.027 0.0807The values of the example granular data 214 of the target region 102provided above in Table 1 represent quantities (e.g., counts,percentages, etc.) of members of the target region 102 satisfyingcorresponding demographic constraints of interest that also satisfycorresponding behavioral constraints of interest. For example, asprovided above in Table 1, the example granular data 214 calculated bythe example target region calculator 206 includes a value of ‘0.081’that indicates 8.10% of young females of the example target region 102were tuned to the show “Shameless.” As illustrated in example Table 1provided above, the target region calculator 206 utilizes the minimumcross entropy to reduce variability of small values (e.g., values near,close to and/or approximate ‘0.0’ or 0%) of the target region granulardata 214 relative to the corresponding values of the granular data 212of the source region 104 to increase accuracy and/or certainty of thosevalues of the target region granular data 214. For example, the valuesof the target region granular data 214 of Table 1 for the show “ChicagoFire” and old females (e.g., ‘0.0383’), young males (e.g., ‘0.027’), andold males (e.g., ‘0.0165’) are approximate to the respective values ofthe granular data 212 of the source region 104 (e.g., ‘0.03’ for oldfemales, ‘0.01’ for young males, ‘0.01’ for old males as illustrated inFIG. 2).

The example demographics estimator 140 of FIGS. 1 and/or 2 enables theexample AME 106 or other entity to determine the granular data 214 ofthe example target region 102 by utilizing the minimum cross entropybased on the example aggregate demographics data 208 and the exampleaggregate tuning data 210 of the target region 102, thereby reducing anamount of data collected from the target region 102 by computernetworked data collection systems. For example, the demographicsestimator 140 enables the example AME 106 or other entity to utilize theminimum cross entropy to determine the granular data 214 of the exampletarget region 102 based on non-person-specific tuning data collectedfrom STBs (e.g., the example STBs 118 a, 118 b of FIG. 1) of the targetregion 102 and non-person-specific and non-household-specific censusdata without having to collect person-specific demographics andbehavioral data from panelists of the target region.

While an example manner of implementing the demographics estimator 140of FIG. 1 is illustrated in FIG. 2, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example target region determiner 202, the example sourceregion determiner 204, the example target region calculator 206 and/or,more generally, the example demographics estimator 140 of FIG. 1 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample target region determiner 202, the example source regiondeterminer 204, the example target region calculator 206 and/or, moregenerally, the example demographics estimator 140 could be implementedby one or more analog or digital circuit(s), logic circuits,programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example targetregion determiner 202, the example source region determiner 204, theexample target region calculator 206 and/or, the example demographicsestimator 140 is/are hereby expressly defined to include a tangiblecomputer readable storage device or storage disk such as a memory, adigital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc.storing the software and/or firmware. Further still, the exampledemographics estimator 140 of FIG. 1 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 2, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

A flowchart representative of example machine readable instructions forimplementing the demographics estimator 140 of FIG. 1 is shown in FIG.3. A flowchart representative of example machine readable instructionsfor implementing the target region calculator 206 of FIG. 2 is shown inFIG. 4. In this example, the machine readable instructions comprise aprogram for execution by a processor such as the processor 912 shown inthe example processor platform 900 discussed below in connection withFIG. 9. The program may be embodied in software stored on a tangiblecomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), a Blu-ray disk, or a memoryassociated with the processor 912, but the entire program and/or partsthereof could alternatively be executed by a device other than theprocessor 912 and/or embodied in firmware or dedicated hardware.Further, although the example program is described with reference to theflowchart illustrated in FIGS. 3 and 4, many other methods ofimplementing the example demographics estimator 140 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined.

As mentioned above, the example processes of FIGS. 3 and 4 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 3 and 4 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIG. 3 is a flow diagram representative of example machine readableinstructions 300 that may be executed to implement the exampledemographics estimator 140 of FIGS. 1 and/or 2 to utilize the minimumcross entropy to determine the granular data 214 of the target region102 of FIG. 1. Initially, at block 302, the example target regiondeterminer 202 determines the example aggregate demographics data 208 ofthe example target region 102. For example, the aggregate demographicsdata 208 determined by the target region determiner 202 is in vectorform in which elements represent values indicative of quantities (e.g.,counts, percentages) of the members (e.g., the example members 114 a,114 b, 114 c of FIG. 1) of the target region 102 that are associatedwith demographic constraints of interest (e.g., a “young female”constraint, an “old female” constraint, a “young male” constraint, an“old male” constraint). For example, the aggregate demographics data 208determined by the example aggregate demographics data 208 is normalizedto a value of ‘1.0’ such that the sum of the elements of the aggregatedemographics data 210 equals a value of ‘1.0.’

At block 304, the example target region determiner 202 determines theaggregate tuning data 210 (e.g., aggregate behavioral data) of thetarget region 102. The example target region determiner 202 determinesthe example aggregate tuning data 210 in vector form in which elementsrepresent values indicative of quantities (e.g., counts, percentages) ofthe households (e.g., the example households 110 a, 110 b of FIG. 1) ofthe target region 102 that are associated with behavioral constraints(e.g., tuning events) of interest (e.g., a constraint for the show“Shameless,” a constraint for the show “Chicago Fire,” a constraint forthe show “The Good Wife”).

At block 306, the example source region determiner 204 determines theexample granular data 212 of the example source region 104. The examplesource region determiner 204 determines the example granular data 212 inmatrix form such that rows correspond to behavioral constraints ofinterest, columns correspond to demographic constraints of interest, andelements represent values indicative of quantities (e.g., counts,percentages, ratings points, ratings shares, etc.) of members of thesource region 104 satisfying the corresponding behavioral constraintsthat also satisfy the corresponding demographic constraints. Forexample, the granular data 212 determined by the source regiondeterminer 204 includes data for the same demographic constraints as theexample aggregate demographics data 208 and the same behavioralconstraints as the example aggregate tuning data 210.

At block 308, the example target region calculator 206 defines anon-linear constraint (e.g., an optimization constraint). The exampletarget region calculator 206 defines a non-linear constraint based onthe example aggregate demographics data 208 and/or the example aggregatetuning data 210 determined at blocks 302, 304, respectively, of FIG. 3.For example, the target region calculator 206 defines a non-linearconstraint as provided below in Equation 3.

Pw=C   Equation 3

In Equation 3 provided above, P represents the granular data 214 of thetarget region 102 to be calculated (e.g., P includes p₁, p₂, p₃, etc.),w represents the aggregate demographics data 208 of the target region102, and C represents the aggregate tuning data 210 of the target region102.

Further, the example target region calculator 206 determines whetherthere is another non-linear constraint (e.g., another optimizationconstraint) to be defined (block 310). If there is another non-linearconstraint, the target region calculator 206 repeats blocks 308, 310until no other non-linear constraints remain to be defined. Upondetermining the non-linear constraints, the example target regioncalculator 206 constructs a minimum cross entropy relationship based onthe granular data 212 of the source region 104 and the aggregatedemographics data 208 of the target region 102. The target regioncalculator 206 constructs the minimum cross entropy relationship asprovided below in Equation 4.

$\begin{matrix}{\frac{p_{i}}{1 - p_{i}} = {\frac{q_{i}}{1 - q_{i}}e^{{- \lambda}\; w_{i}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In Equation 4 provided above, p_(i) represents the target regiongranular data 214 of the behavioral constraint i (e.g., in which apercentage of a population of the target region 102 is represented indecimal form), q_(i) represents the granular data 212 of the behavioralconstraint i (e.g., in which a percentage of a population of the sourceregion 104 is represented in decimal form), and w_(i) represents theaggregate demographics data 208 associated with the behavioralconstraint i (e.g., in which a percentage of a population of the targetregion 102 is represented in decimal form). The relationship of equation4 is constructed such that each value of p_(i) will be a positive valuebetween ‘0.0’ and ‘1.0’ (e.g., between 0% and 100% if represented as apercentage). That is, the relationship of equation 4 is constructed suchthat 0≦p_(i)≦1.0.

At block 314, the example target region calculator 206 identifies abehavioral constraint (e.g., a constraint associated with a tuningevent) of the aggregate tuning data 210 and the granular data 212. Forexample, the target region calculator 206 identifies a behavioralconstraint (e.g., the constraint for the show “Shameless”) associatedwith the first row of the aggregate tuning data 210 and the first row ofthe granular data 212.

At block 316, the example target region calculator 206 calculates ordetermines the granular data 214 of the target region 102 for thebehavioral constraint identified at block 314 via minimum cross entropy.The example target region calculator 206 determines portions (e.g.,counts, percentages) of members of the example target region 102satisfying the identified behavioral constraint that also satisfy thecorresponding demographic constraints associated with the exampleaggregate demographics data 208 and the granular data 212 of the sourceregion 104. For example, the target region calculator 206 determinesthat 8.10% of young females, 4.02% of old females, 10.16% of youngmales, and 3.03% of old males of the example target region 102 wereexposed to the show “Shameless.”

Upon the example target region calculator 206 calculating the granulardata 214 for the identified behavioral constraint, the example targetregion calculator 206 determines whether there is another behavioralconstraint to be identified (block 318). If the target region calculator206 determines that there are other behavioral constraints, the targetregion calculator 206 repeats blocks 314, 316, 318 until no otherbehavioral constraints remain. For example, the target region calculator206 repeats blocks 314, 316, 318 for the constraint associated with theshow “Chicago Fire,” the constraint associated with the show “The GoodWife.”

If the example target region calculator 206 determines that there are noother behavioral constraints, the target region calculator 206 sets theexample granular data 214 of the target region 102 (block 320). Forexample, the target region calculator 208 sets the granular data 214 ofthe target region 102 by integrating together the granular data 214calculated by the target region calculator 206 at block 316 for therespective behavioral constraints identified by the target regioncalculator 206 at block 314. For example, the target region calculator206 sets the granular data 214 of the target region 102 that weredetermined via the calculated minimum cross entropies at block 316 asshown below in Table 2.

TABLE 2 Young Female Old Female Young Male Old Male Shameless 0.0810.0402 0.1016 0.0303 Chicago Fire 0.2736 0.0383 0.0713 0.0407 The GoodWife 0.0126 0.0431 0.027 0.0807The values provided above in example Table 2 represent quantities (e.g.,counts, percentages, etc.) of members of the target region 102satisfying corresponding demographic constraints of interest that alsosatisfy corresponding behavioral constraints of interest. For example,Table 2 shows the example target region calculator 206 calculated avalue of ‘0.081’ that indicates 8.10% of young females of the exampletarget region 102 were tuned to the show “Shameless.” As illustrated inTable 2, the granular data 214 calculated by the target regioncalculator 206 reduces variability of small values of the target regiongranular data 214 relative to the corresponding values of the granulardata 212 of the source region 104 to increase accuracy and/or certaintyof those values of the target region granular data 214. For example, thevalues of the target region granular data 214 for the show “ChicagoFire” and old females (e.g., ‘0.0383’), young males (e.g., ‘0.027’), andold males (e.g., ‘0.0165’) that are represented by Table 2 areapproximate to the respective values for the show “Chicago Fire” and oldfemales (e.g., ‘0.03’), young males (e.g., ‘0.01’), and old males (e.g.,‘0.01’) of the granular data 212 of the source region 104 as illustratedin FIG. 2.

FIG. 4 is a flow diagram representative of example machine readableinstructions 316 that may be executed to implement the example targetregion calculator 206 of FIG. 2 to determine the granular data 214 ofthe target region 102 of FIG. 1 for the behavioral constraint identifiedat block 314. For example, the instructions 316 illustrated by the flowdiagram of FIG. 4 may implement block 316 of FIG. 3.

Initially, at block 402, the example target region calculator 206utilizes the minimum cross entropy to apportion the aggregate behavioraldata 210 of the behavioral constraint identified at block 314 of FIG. 3.For example, the target region calculator 206 utilizes the minimum crossentropy (e.g., calculated via non-linear optimization) by solving for A.of Equation 4 that was constructed at block 312 of FIG. 3. By utilizingthe minimum cross entropy, the example target region calculator 206apportions the value of the aggregate tuning data 210 associated withthe identified behavioral constraint (e.g., the constraint for the show“Shameless”) among the demographic constraints associated with theaggregate demographics data 208 and the granular data 212 (e.g., the“young female” constraint, the “old female” constraint, the “young male”constraint, and the “old male” constraint).

At block 404, the example target region calculator 206 identifies one ofthe demographic constraints associated with the aggregate demographicsdata 208 and the granular data 212. For example, the target regioncalculator 206 identifies the “young female” demographic constraint. Theexample target region calculator 206 determines a value for a quantity(e.g., a count, a percentage) of members of the target region 102 (e.g.,the example members 114 a, 114 b, 114 c of FIG. 1) satisfying theidentified demographic constraint that also satisfy the identifiedbehavioral constraint (block 406). For example, the target regioncalculator 206 determines a quantity of ‘0.081’ that indicates 8.10% ofyoung females of the target region 102 were exposed to the show“Shameless.”

At block 408, the example target region calculator 206 determineswhether there is another demographic constraint to identify. Forexample, the target region calculator 206 determines whether there isanother demographic constraint associated with the aggregatedemographics data 208 and the granular data 212. If the target regioncalculator 206 determines that there is another demographic constraint,the target region calculator 206 repeats blocks 404, 406, 408 for theother demographic constraints. For example, the target region calculator206 repeats blocks 404, 406, 408 for the “old female” constraint, the“young male” constraint, and the “old male” constraint. If the exampletarget region calculator 206 determines that there are no otherconstraints, the target region calculator 206 sets the values determinedat block 402 as the granular data 214 of the target region 102 for thebehavioral constraint identified at block 314 (block 410). For example,the target region calculator 206 sets the granular data 214 for theidentified behavioral constraint as shown below in Table 3.

TABLE 3 Young Female Old Female Young Male Old Male Shameless 8.10 4.0210.16 3.03

FIGS. 5-8 describe an example environment 500 in which an OnlineCampaign Ratings (OCR) system and/or a Digital Ad Rating (DAR) systemdeveloped by The Nielsen Company (US), LLC is employed to monitor onlineactivity. In the environment 500 in which the OCR and/or DAR system isemployed, beacon instructions are downloaded to a client (e.g., a mediapresentation device) when the client requests media. The beaconinstructions are, thus, executed whenever the media is accessed, be itfrom a server or from a cache. The beacon instructions cause monitoringdata reflecting information about the access to the media to be sentfrom the client that downloaded the media to a monitoring entity (e.g.,an audience measurement entity). Because the beaconing instructions areassociated with the media and executed by a client browser whenever themedia is accessed, the monitoring information is provided to the AMEirrespective of whether the client is a panelist of the AME.

The disclosed methods, apparatus and articles of manufacture of FIGS.5-8 enable the AME to calculate scaling values or weights that correctfor online impressions that are not associated with demographicconstraints of interest (e.g., non-count or under-representation). Asdescribed in further detail below, FIG. 5 is a block diagram of theexample environment 500 in which an OCR system and/or DAR system isemployed for online media campaign measurement. The example environment500 of FIG. 5 includes a region (e.g., a target region 502) in whichonline activity is monitored and a sub-region of panelists (e.g., asource region 504) of the region. Further, FIG. 6 is a block diagram ofan example environment 600 in which an example media presentation devicereports audience impressions of media to impression collection entitiesto facilitate audience measurement, FIG. 7 is an example communicationflow diagram illustrating collection of data in an OCR and/or DARsystem, and FIG. 8 is a block diagram of an example implementation of anexample demographics estimator that is to utilize minimum cross entropyto calculate or determine scaling values or weights to correct fornon-count.

FIG. 5 is a block diagram of the example environment 500 that includesthe example target region 502, the example source region 504, theexample AME 106, and the example network 108. The example AME 106 andthe example network 108 of FIG. 5 are substantially similar to oridentical to those components having the same reference numbers in FIG.1, are described above in further detail in connection with FIG. 1, andwill not be described in detail again.

In the illustrated example of FIG. 5, the target region 502 (e.g., apopulation) includes households 506 a, 506 b (e.g., non-panelisthouseholds) and households 506 c, 506 d (e.g., panelist households), andthe source region 504 (e.g., a sub-region of the population) includesthe households 506 c, 506 d (e.g., panelist households).

The example households 506 a, 506 b, 506 c, 506 d of the exampleenvironment 500 include example members 508 a, 508 b, 508 c, 508 d, 508e, 508 f and example media presentation devices 510 a, 510 b, 510 c, 510d. For example, the household 506 a includes the members 508 a, 508 band the media presentation device 510 a, the household 506 b includesthe member 508 c and the media presentation device 510 b, the household506 c includes the members 508 d, 508 e and the media presentationdevice 510 c, and the household 506 d includes the member 508 f and themedia presentation device 510 d.

In some examples, the households 506 a, 506 b, 506 c, 506 d arerepresentative of many other households (e.g., other households of anon-panelist region) that may be included in the example target region502. Additionally or alternatively, the households 506 c, 506 d arerepresentative of many other households (e.g., other panelisthouseholds) that may be included in the example source region 504.Characteristics of the other households (e.g., a number of householdmembers, demographics of the household members, a number of televisions,etc.) may be similar to and/or different from those of therepresentative households 506 a, 506 b, 506 c, 506 d. For example, otherhouseholds include one member, two members, three members, four members,etc.

The media presentation devices 510 a, 510 b, 510 c, 510 d (e.g., clientdevices) of the illustrated example include devices capable of accessingmedia over a network. For example, the media presentation devices 510 a510 b, 510 c, 510 d include computers, tablets, mobile devices, smarttelevisions, or other Internet-capable devices or appliances. Theexample media presentation devices 510 a, 510 b, 510 c, 510 d are usedto collect corresponding example impression data 512 a, 512 b, 512 c,512 d (e.g., behavioral data) for media accessed via the mediapresentation devices 510 a, 510 b, 510 c, 510 d.

Further, as illustrated in FIG. 5, the example members 508 d, 508 e, 508f (e.g., panelists) of the example source region 504 (e.g., a panelistsub-region of the population) provide respective example demographicsdata 514 a, 514 b, 514 c. For example, the demographics data 514 aincludes person-specific information associated with the member 508 d,the demographics data 514 b includes person-specific informationassociated with the member 508 e, and the demographics data 514 cincludes person-specific information associated with the member 508 f.The demographics data 514 a, 514 b, 514 c of the illustrated exampleidentify which demographic constraints (e.g., demographic marginals ofrespective demographic dimensions, combinations of demographic marginalsof combinations of respective demographic dimensions, etc.) areassociated with the corresponding members 508 d, 508 e, 508 f of thesource region 104. For example, the demographics data 514 a indicatesthat the member 508 d satisfies the “male” demographic constraint, thedemographics data 514 b indicates that the member 508 e satisfies the“female” demographic constraint, and the demographics data 514 cindicates that the member 508 f satisfies the “female” demographicconstraint. The demographics data 514 a, 514 b, 514 c may be provided bythe members 508 d, 508 e, 508 f via, for example, self-reporting,responding to surveys, etc.

The example demographics estimator 140 of the AME 106 of FIG. 5 utilizesthe collected impressions data 512 a, 512 b, 512 c, 512 d of the targetregion 502 (e.g., the population), the demographics data 514 a, 514 b,514 c of the source region 504 (e.g., the panelist sub-region of thepopulation), and demographics data of a database proprietor (e.g. adatabase proprietor 608 of FIGS. 6 and 7) to utilize a minimum crossentropy to calculate or determine scaling values or weights fordemographic constraints of interest (e.g., granular data) for the targetregion 502. For example, the demographics estimator 140 determines thescaling values by utilizing the minimum cross entropy to determinequantities of impressions of the example target region 502 that areassociated with demographics constraints of interest (e.g., the “male”demographic constraint, the “female” demographic constraint).

In some examples, the AME 106, the database proprietor (e.g. thedatabase proprietor 608 of FIGS. 6 and 7) and/or the other entityassociates an impression of online activity from the target region 502with demographics of a person (e.g., the example members 508 a, 508 b,508 c, 508 d, 508 e, 5080 corresponding to the impression. In theillustrated example, the example target region demographics database 134stores aggregate demographics data for members (e.g., the examplemembers 508 a, 508 b, 508 c, 508 d, 508 e, 5080 of the target region502. For example, the aggregate demographics data stored by the targetregion demographics database 134 are obtained from a database proprietor(e.g., Facebook, Twitter, MySpace, Yahoo!, Google, Amazon.com, Buy.com,Experian, etc.) that has collected the demographics data from themembers of the target region 502. Further, the example target regionbehavioral database 136 stores the recorded impressions of onlineactivity (e.g., aggregate behavioral data) of the target region 502. Forexample, the target region behavioral database 136 stores the exampleimpressions data 512 a, 512 b, 512 c, 512 d collected from the examplemedia presentation devices 510 a, 510 b, 510 c, 510 d of the targetregion 502.

Further, based on the demographics data (e.g., the example demographicsdata 514 a, 514 b, 514 c) collected from the panelists (e.g., theexample members 508 d, 508 e, 5080 of the source region 504 (e.g., thepanelist sub-region of the population), the example AME 106, thedatabase proprietor and/or another entity identifies a quantity (e.g., acount, a percentage) of impressions of online activity associated withpanelists (e.g., the example members 508 d, 508 e, 5080 for whichcorresponding demographic constraints of interest are identified. Forexample, the AME 106, the database proprietor and/or the other entitydetermines that 50% of impressions deriving from a male panelist arerecorded as being associated with a male, and 75% of impressionsderiving from a female panelist are recorded as being associated with afemale. The example source region database 138 of FIG. 5 stores therecorded impressions of online activity and the demographics associatedwith the recorded impressions (e.g., granular data) of the examplesource region 504.

In some examples, the AME 106, a database proprietor and/or anotherentity are unable to associate a recorded impression with a demographicconstraint of interest, thereby resulting in incomplete demographicimpression data (e.g., data indicating characteristics of the peopleassociated with the corresponding recorded impressions) of the targetregion 502.

Based on the data stored in the target region demographics database 134,the target region behavioral database 136, and the source regiondatabase 138, the example demographics estimator 140 determines scalingvalues or weights for the example target region 502 (e.g., granular dataof the target region 502) by utilizing minimum cross entropy todetermine quantities of impressions of online activity associated withthe demographic constraints of interest.

FIG. 6 is a block diagram of the example environment 600 in which theexample media presentation device 510 a of the source region of FIG. 5reports audience impressions of media to impression collection entities602 to facilitate identifying total impressions and sizes of uniqueaudiences exposed to different media. As used herein, the termimpression collection entity refers to any entity that collectsimpression data. In the illustrated example, the media presentationdevice 510 a employs a web browser and/or applications (e.g., apps) toaccess media, some of which include instructions that cause the mediapresentation device 510 a to report media monitoring information to oneor more of the impression collection entities 602. That is, when themedia presentation device 510 a of the illustrated example accessesmedia, a web browser and/or application of the media presentation device510 a executes instructions in the media to send a beacon request orimpression request 604 to one or more of the impression collectionentities 602 via, for example, the Internet 606. The beacon requests 604of the illustrated example include information about accesses to mediaat the media presentation device 510 a. Such beacon requests 604 allowmonitoring entities, such as the impression collection entities 602, tocollect impressions for different media accessed via the mediapresentation device 510 a. In this manner, the impression collectionentities 602 can generate large impression quantities for differentmedia (e.g., different content and/or advertisement campaigns).

The impression collection entities 602 of the illustrated exampleinclude the AME 106 and an example database proprietor (DP) 608. In theillustrated example, the AME 106 does not provide the media to the mediapresentation device 510 a and is a trusted (e.g., neutral) third party(e.g., The Nielsen Company, LLC) for providing accurate media accessstatistics. In the illustrated example, the database proprietor 608 isone of many database proprietors that operates on the Internet toprovide services to large numbers of subscribers. Such services may beemail services, social networking services, news media services, cloudstorage services, streaming music services, streaming video services,online retail shopping services, credit monitoring services, etc.Example database proprietors include social network sites (e.g.,Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!,Google, etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.),credit reporting services (e.g., Experian) and/or any other webservice(s) site that maintains user registration records. In examplesdisclosed herein, the database proprietor 608 maintains user accountrecords corresponding to users registered for Internet-based servicesprovided by the database proprietors. That is, in exchange for theprovision of services, subscribers register with the database proprietor608. As part of this registration, the subscribers provide detaileddemographic information to the database proprietor 608. Demographicinformation may include, for example, gender, age, ethnicity, income,home location, education level, occupation, etc. In the illustratedexample, the database proprietor 608 sets a device/user identifier(e.g., an identifier described below in connection with FIG. 7) on asubscriber's media presentation device 510 a that enables the databaseproprietor 608 to identify the subscriber.

In the illustrated example, when the database proprietor 608 receives abeacon/impression request 604 from the media presentation device 510 a,the database proprietor 608 requests the media presentation device 510 ato provide the device/user identifier that the database proprietor 608had previously set for the media presentation device 510 a. The databaseproprietor 608 uses the device/user identifier corresponding to themedia presentation device 510 a to identify demographic information inits user account records corresponding to the subscriber of the mediapresentation device 510 a. In this manner, the database proprietor 608can generate demographic impressions by associating demographicinformation with an audience impression for the media accessed at themedia presentation device 510 a. As explained above, a demographicimpression is an impression that is associated with a characteristic(e.g., a demographic characteristic) of the person exposed to the media.

In some examples, the database proprietor 608 is unable to identify thedevice/user identifier corresponding to the media presentation device510 a in its user account records. As a result, the database proprietor608 is unable to identify demographic information from its user accountrecords that correspond to the media presentation device 510 a and/orthe members 508 a, 508 b using the media presentation device 510 a forthe received beacon/impression request 604. In such examples, thedatabase proprietor 608 records the received beacon/impression request604 in a total impression count but does not record thebeacon/impression request 604 in an impression count for a demographicconstraint of interest (e.g., a “male” constraint, a “female”constraint). As used herein, a “non-count” or an “under-representation”refers to an impression that is recorded in a total impression count butis not recorded in an impression count for a demographic constraint ofinterest (e.g., a demographic impression).

Further, in some examples, the AME 106 establishes an AME panel of users(e.g., the example members 508 d, 508 e, 508 f of the example sourceregion 504 of FIG. 5) who have agreed to provide their demographicinformation and to have their Internet browsing activities monitored.Those members 508 d, 508 e, 508 f provide detailed informationconcerning the person's identity and demographics (e.g., thecorresponding example demographics data 514 a, 514 b, 514 c of FIG. 5)to the AME 106. The AME 106 sets a device/user identifier (e.g., anidentifier described below in connection with FIG. 7) on the mediapresentation device (e.g., the example media presentation devices 510 c,510 d of FIG. 5) corresponding to the panelist (e.g., the members 508 d,508 e, 5080 that enables the AME 106 to identify the panelist. An AMEpanel may be a cross-platform home television/computer (TVPC) panelbuilt and maintained by the AME 106. In other examples, the AME panelmay be a computer panel or internet-device panel without correspondingto a television audience panel. In yet other examples, the AME panel maybe a cross-platform radio/computer panel and/or a panel formed for othermediums.

In such examples, when the AME 106 receives a beacon request 604 fromthe media presentation device (e.g., the media presentation devices 510c, 510 d) of the source region 504, the AME 106 requests the mediapresentation device to provide the AME 106 with the device/useridentifier that the AME 106 previously set in the media presentationdevice. The AME 106 uses the device/user identifier corresponding to themedia presentation device 510 a to identify demographic information inits user records corresponding to the panelist of the media presentationdevice of the source region 504. In this manner, the AME 106 cangenerate demographic impressions (e.g., granular data of the sourceregion 504) by associating demographic information (e.g., the exampledemographics data 514 a, 514 b, 514 c of the source region 504 of FIG.5) with an audience impression (e.g., the example impressions data 512c, 512 d of the source region 504 of FIG. 5) for the media accessed inthe source region. In some examples, members (e.g., the members 508 d,508 e) share a media presentation device (e.g., the media presentationdevice 510 c) to access the internet-based service of the databaseproprietor 608 and to access other media via the Internet 606. In theillustrated example, when the database proprietor 608 receives abeacon/impression request 604 for media accessed via the mediapresentation device 510 c, the database proprietor 608 logs animpression for the media access as corresponding to the member 508 d,508 e of the household 506 c that most recently logged into the databaseproprietor 608.

FIG. 7 is an example communication flow diagram illustrating an examplemanner in which the audience measurement entity 106 and the exampledatabase proprietor 608 collect data from the media presentation device510 a of the example source region 504. For example, FIG. 7 illustratesan example manner in which the AME 106 and the database proprietor 608of FIG. 6 can collect impressions and demographic information based onthe media presentation device 510 a reporting impressions to the AME 106and the database proprietor 608. In the illustrated example, thedemographics estimator 140 is to correct for non-count orunder-representation by the database proprietor 608. The example chainof events shown in FIG. 7 occurs when the media presentation device 510a accesses media for which the media presentation device 510 a reportsan impression to the AME 106 and the database proprietor 608. In someexamples, the media presentation device 510 a reports impressions foraccessed media based on instructions (e.g., beacon instructions)embedded in the media that instruct the media presentation device 510 a(e.g., instruct a web browser or an app in the media presentation device510 a) to send beacon/impression requests (e.g., the beacon/impressionrequests 604 of FIG. 6) to the AME 106 and/or the database proprietor608. In such examples, the media having the beacon instructions isreferred to as tagged media. In other examples, the media presentationdevice 510 a reports impressions for accessed media based oninstructions embedded in apps or web browsers that execute on the mediapresentation device 510 a to send beacon/impression requests (e.g., thebeacon/impression requests 604 of FIG. 6) to the AME 106, and/or thedatabase proprietor 608 for corresponding media accessed via those appsor web browsers. In any case, the beacon/impression requests (e.g., thebeacon/impression requests 604 of FIG. 6) include device/useridentifiers (e.g., AME IDs and/or DP IDs) as described further below toallow the corresponding AME 106 and/or database proprietor 608 toassociate demographic information with resulting logged impressions.

In the illustrated example, the media presentation device 510 a accessesmedia 702 tagged with beacon instructions 704. The beacon instructions704 cause the media presentation device 510 a to send abeacon/impression request 706 to an AME impressions collector 708 whenthe media presentation device 510 a accesses the media 702. For example,a web browser and/or app of the media presentation device 510 a executesthe beacon instructions 704 in the media 702 which instruct the browserand/or app to generate and send the beacon/impression request 706. Inthe illustrated example, the media presentation device 510 a sends thebeacon/impression request 706 to the AME impression collector 708 usingan HTTP (hypertext transfer protocol) request addressed to the URL(uniform resource locator) of the AME impressions collector 708 at, forexample, a first internet domain of the AME 106. The beacon/impressionrequest 706 of the illustrated example includes a media identifier 710(e.g., an identifier that can be used to identify content, anadvertisement, and/or any other media) corresponding to the media 702.In some examples, the beacon/impression request 706 also includes a siteidentifier (e.g., a URL) of the website that served the media 702 to themedia presentation device 510 a and/or a host website ID (e.g.,www.acme.com) of the website that displays or presents the media 702. Inthe illustrated example, the beacon/impression request 706 includes adevice/user identifier 712. In the illustrated example, the device/useridentifier 712 that the media presentation device 510 a provides in thebeacon impression request 706 is an AME ID because it corresponds to anidentifier that the AME 106 uses to identify a user (e.g., the examplemembers 508 a, 508 b of FIG. 5) corresponding to the media presentationdevice 510 a. In other examples, the media presentation device 510 a maynot send the device/user identifier 712 until the media presentationdevice 510 a receives a request for the same from a server of the AME106 (e.g., in response to, for example, the AME impressions collector708 receiving the beacon/impression request 706).

In some examples, the device/user identifier 712 may be a deviceidentifier (e.g., an international mobile equipment identity (IMEI), amobile equipment identifier (MEID), a media access control (MAC)address, etc.), a web browser unique identifier (e.g., a cookie), a useridentifier (e.g., a user name, a login ID, etc.), an Adobe Flash® clientidentifier, identification information stored in an HTML5 datastore,and/or any other identifier that the AME 106 stores in association withdemographic information about users of the media presentation devices(e.g., the media presentation devices 510 a, 510 b, 510 c of FIG. 5).When the AME 106 receives the device/user identifier 712, the AME 106can obtain demographic information corresponding to a user of the mediapresentation device 510 a based on the device/user identifier 712 thatthe AME 106 receives from the media presentation device 510 a. In someexamples, the device/user identifier 712 may be encrypted (e.g., hashed)at the media presentation device 510 a so that only an intended finalrecipient of the device/user identifier 712 can decrypt the hashedidentifier 712. For example, if the device/user identifier 712 is acookie that is set in the media presentation device 510 a by the AME106, the device/user identifier 712 can be hashed so that only the AME106 can decrypt the device/user identifier 712. If the device/useridentifier 712 is an IMEI number, the media presentation device 510 acan hash the device/user identifier 712 so that only a wireless carrier(e.g., the database proprietor 608) can decrypt the hashed identifier712 to recover the IMEI for use in accessing demographic informationcorresponding to the user of the media presentation device 510 a. Byhashing the device/user identifier 712, an intermediate party (e.g., anintermediate server or entity on the Internet) receiving the beaconrequest cannot directly identify a user of the media presentation device510 a.

In response to receiving the beacon/impression request 706, the AMEimpressions collector 708 logs an impression for the media 702 bystoring the media identifier 710 contained in the beacon/impressionrequest 706. In the illustrated example of FIG. 7, the AME impressionscollector 708 also uses the device/user identifier 712 in thebeacon/impression request 706 to identify AME panelist demographicinformation corresponding to a panelist of the media presentation device510 a. That is, the device/user identifier 712 matches a user ID of apanelist member (e.g., a panelist corresponding to a panelist profilemaintained and/or stored by the AME 106). In this manner, the AMEimpressions collector 708 can associate the logged impression withdemographic information of a panelist corresponding to the mediapresentation device 510 a. Additionally or alternatively, the AME 106may obtain demographics information from the database proprietor 608 forthe logged impression if the media presentation device 510 a correspondsto a subscriber of the database proprietor 608.

In the illustrated example of FIG. 7, to compare or supplement panelistdemographics (e.g., for accuracy or completeness) of the AME 106 withdemographics from one or more database proprietors (e.g., the databaseproprietor 608), the AME impressions collector 708 returns a beaconresponse message 714 (e.g., a first beacon response) to the mediapresentation device 510 a including an HTTP “302 Found” re-directmessage and a URL of a participating database proprietor 608 at, forexample, a second internet domain. In the illustrated example, the HTTP“302 Found” re-direct message in the beacon response 714 instructs themedia presentation device 510 a to send a second beacon request 716 tothe database proprietor 608. In other examples, instead of using an HTTP“302 Found” re-direct message, redirects may be implemented using, forexample, an iframe source instruction (e.g., <iframe src=“ ”>) or anyother instruction that can instruct a client device to send a subsequentbeacon request (e.g., the second beacon request 716) to a participatingdatabase proprietor 608. In the illustrated example, the AME impressionscollector 708 determines the database proprietor 608 specified in thebeacon response 714 using a rule and/or any other suitable type ofselection criteria or process. In some examples, the AME impressionscollector 708 determines a particular database proprietor to which toredirect a beacon request based on, for example, empirical dataindicative of which database proprietor is most likely to havedemographic data for a user corresponding to the device/user identifier712. In some examples, the beacon instructions 704 include a predefinedURL of one or more database proprietors to which the media presentationdevice 510 a should send follow up beacon requests 716. In otherexamples, the same database proprietor is always identified in the firstredirect message (e.g., the beacon response 714).

In the illustrated example of FIG. 7, the beacon/impression request 716may include a device/user identifier 718 that is a DP ID because it isused by the database proprietor 608 to identify a subscriber of themedia presentation device 510 a when logging an impression. In someinstances (e.g., in which the database proprietor 608 has not yet set aDP ID in the media presentation device 510 a), the beacon/impressionrequest 716 does not include the device/user identifier 718. In someexamples, the DP ID is not sent until the DP requests the same (e.g., inresponse to the beacon/impression request 716). In some examples, thedevice/user identifier 718 is a device identifier (e.g., aninternational mobile equipment identity (IMEI), a mobile equipmentidentifier (MEID), a media access control (MAC) address, etc.), a webbrowser unique identifier (e.g., a cookie), a user identifier (e.g., auser name, a login ID, etc.), an Adobe Flash® client identifier,identification information stored in an HTML5 datastore, and/or anyother identifier that the database proprietor 608 stores in associationwith demographic information about subscribers corresponding to themedia presentation devices (e.g., the example media presentation devices510 a, 510 b, 510 c of FIG. 5). When the database proprietor 608receives the device/user identifier 718, the database proprietor 608 canobtain demographic information corresponding to a user of the mediapresentation device 510 a based on the device/user identifier 718 thatthe database proprietor 608 receives from the media presentation device510 a. In some examples, the device/user identifier 718 may be encrypted(e.g., hashed) at the media presentation device 510 a so that only anintended final recipient of the device/user identifier 718 can decryptthe hashed identifier 718. For example, if the device/user identifier718 is a cookie that is set in the media presentation device 510 a bythe database proprietor 608, the device/user identifier 718 can behashed so that only the database proprietor 608 can decrypt thedevice/user identifier 718. If the device/user identifier 718 is an IMEInumber, the media presentation device 510 a can hash the device/useridentifier 718 so that only a wireless carrier (e.g., the databaseproprietor 608) can decrypt the hashed identifier 718 to recover theIMEI for use in accessing demographic information corresponding to theuser of the media presentation device 510 a. By hashing the device/useridentifier 718, an intermediate party (e.g., an intermediate server orentity on the Internet) receiving the beacon request cannot directlyidentify a user of the media presentation device 510 a. For example, ifthe intended final recipient of the device/user identifier 718 is thedatabase proprietor 608, the AME 106 cannot recover identifierinformation when the device/user identifier 718 is hashed by the mediapresentation device 510 a for decrypting only by the intended databaseproprietor 608.

In some examples that use cookies as the device/user identifier 718,when a user deletes a database proprietor cookie from the mediapresentation device 510 a, the database proprietor 608 sets the samecookie value in the media presentation device 510 a the next time theuser logs into a service of the database proprietor 608. In suchexamples, the cookies used by the database proprietor 608 areregistration-based cookies, which facilitate setting the same cookievalue after a deletion of the cookie value has occurred on the mediapresentation device 510 a. In this manner, the database proprietor 608can collect impressions for the media presentation device 510 a based onthe same cookie value over time to generate unique audience (UA) sizeswhile eliminating or substantially reducing the likelihood that a singleunique person will be counted as two or more separate unique audiencemembers.

Although only a single database proprietor 608 is shown in FIGS. 6 and7, the impression reporting/collection process of FIGS. 6 and 7 may beimplemented using multiple database proprietors. In some such examples,the beacon instructions 704 cause the media presentation device 510 a tosend beacon/impression requests 716 to numerous database proprietors.For example, the beacon instructions 704 may cause the mediapresentation device 510 a to send the beacon/impression requests 716 tothe numerous database proprietors in parallel or in daisy chain fashion.In some such examples, the beacon instructions 704 cause the mediapresentation device 510 a to stop sending beacon/impression requests 716to database proprietors once a database proprietor has recognized themedia presentation device 510 a. In other examples, the beaconinstructions 704 cause the media presentation device 510 a to sendbeacon/impression requests 716 to database proprietors so that multipledatabase proprietors can recognize the media presentation device 510 aand log a corresponding impression. In any case, multiple databaseproprietors are provided the opportunity to log impressions and providecorresponding demographics information if the user of the mediapresentation device 510 a is a subscriber of services of those databaseproprietors.

In some examples, prior to sending the beacon response 714 to the mediapresentation device 510 a, the AME impressions collector 708 replacessite IDs (e.g., URLs) of media provider(s) that served the media 702with modified site IDs (e.g., substitute site IDs) which are discernableonly by the AME 106 to identify the media provider(s). In some examples,the AME impressions collector 708 may also replace a host website ID(e.g., www.acme.com) with a modified host site ID (e.g., a substitutehost site ID) which is discernable only by the AME 106 as correspondingto the host website via which the media 702 is presented. In someexamples, the AME impressions collector 708 also replaces the mediaidentifier 710 with a modified media identifier 710 corresponding to themedia 702. In this way, the media provider of the media 702, the hostwebsite that presents the media 702, and/or the media identifier 710 areobscured from the database proprietor 608, but the database proprietor608 can still log impressions based on the modified values which canlater be deciphered by the AME 106 after the AME 106 receives loggedimpressions from the database proprietor 608. In some examples, the AMEimpressions collector 708 does not send site IDs, host site IDS, themedia identifier 710 or modified versions thereof in the beacon response714. In such examples, the media presentation device 510 a provides theoriginal, non-modified versions of the media identifier 710, site IDs,host IDs, etc. to the database proprietor 608.

In the illustrated example, the AME impression collector 708 maintains amodified ID mapping table 720 that maps original site IDs with modified(or substitute) site IDs, original host site IDs with modified host siteIDs, and/or maps modified media identifiers to the media identifierssuch as the media identifier 710 to obfuscate or hide such informationfrom database proprietors such as the database proprietor 608. Also inthe illustrated example, the AME impressions collector 708 encrypts allof the information received in the beacon/impression request 706 and themodified information to prevent any intercepting parties from decodingthe information. The AME impressions collector 708 of the illustratedexample sends the encrypted information in the beacon response 714 tothe media presentation device 510 a so that the media presentationdevice 510 a can send the encrypted information to the databaseproprietor 608 in the beacon/impression request 716. In the illustratedexample, the AME impressions collector 708 uses an encryption that canbe decrypted by the database proprietor 608 site specified in the HTTP“302 Found” re-direct message. Periodically or aperiodically, theimpression data collected by the database proprietor 608 is provided toa DP impressions collector 722 of the AME 106 as, for example, batchdata.

Additional examples that may be used to implement the beacon instructionprocesses of FIG. 7 are disclosed in Mainak et al., U.S. Pat. No.8,370,489, which is hereby incorporated herein by reference in itsentirety. In addition, other examples that may be used to implement suchbeacon instructions are disclosed in Blumenau, U.S. Pat. No. 6,108,637,which is hereby incorporated herein by reference in its entirety.

Returning to the example demographics estimator 140, FIG. 8 is a blockdiagram of an example implementation of the demographics estimator 140that is to utilize minimum cross entropy to calculate or determine thescaling values or weights (e.g., granular data) for demographicconstraints of interest to correct for non-count or under-representationof impressions for the target region 502 of FIG. 5. As illustrated inFIG. 8, the example demographics estimator 140 includes the exampletarget region determiner 202, the example source region determiner 204,and the example target region calculator 206. The target regiondeterminer 202, the source region determiner 204, and the target regioncalculator 206 of FIG. 8 are substantially similar or identical to thosecomponents having the same reference numbers in FIG. 2. Those componentsare described above in further detail in connection with FIG. 2 and willnot be described in detail again.

The target region determiner 202 of the illustrated example determinesaggregate demographics data 802 of the example target region 502 (e.g.,a population). For example, the target region determiner 202 collectsthe aggregate demographics data 802 that is based on demographics dataof a database proprietor from the example target region demographicsdatabase 134 of FIG. 5. For example, the target region determiner 202collects the example aggregate demographics data 802 in vector form.Elements of the example aggregate demographics data 802 correspond todemographic constraints of interest. For example, an element of a firstrow of the example aggregate demographics data 802 corresponds with a“male” demographic constraint and an element of a second row correspondswith a “female” demographic constraint. The elements of the exampleaggregate demographics data 802 represent quantities (e.g., counts,percentages) of the target region 502 that match, belong to and/orsatisfy the corresponding demographics of interest. For example, theelement of the first row of the aggregate demographics data 802indicates that 10 recorded impressions of the target region 502 wereassociated with the “male” constraint and 15 recorded impressions of thetarget region 502 were associated with the “female” constraint.

Further, the example target region determiner 202 determines aggregateimpressions data 804 (e.g., aggregate behavioral data) of the exampletarget region 502 (e.g., a population). For example, the target regiondeterminer 202 collects the aggregate impressions data 804 that is basedon the example impressions data 512 a, 512 b, 512 c, 512 d of theexample households 506 a, 506 b, 506 c, 506 d of the target region 502from the example target region behavioral database 136 of FIG. 5. In theillustrated example, the aggregate impression data 804 determined by thetarget region determiner 202 indicates that there were 50 recordedimpressions for the example target region 502. Thus, the aggregatedemographics data 802 and the aggregate impressions data 804 of theillustrated example indicate that 50% of the impressions (e.g., 25impressions associated with a demographic constraint of the 50 totalimpressions) of the target region 502 are not associated with ademographic constraint (e.g., are non-counts or under-representations).

The source region determiner 204 of the illustrated example determinesgranular data 806 of the example source region 504 of FIG. 5. Forexample, the source region determiner 204 collects the granular data 806that is based on impressions data (e.g., the example impressions data512 c, 512 d of FIG. 5) and demographics data (e.g., the exampledemographics data 514 a, 514 b, 514 c of FIG. 5) of panelist households(e.g., the example households 506 a, 506 b of FIG. 5) of the sourceregion 504 (e.g., the panelist sub-region of the population) from theexample source region database 138 of FIG. 5.

As illustrated in FIG. 8, the example source region determiner 204collects the example granular data 806 in vector form. In theillustrated example, rows of the granular data 806 collected by thesource region determiner 204 correspond to behavioral constraints ofinterest. The demographic constraints of the example granular data 214are the same demographic constraints of the example aggregatedemographics data 802. For example, a first row of the granular data 214corresponds with a “male” constraint, and a second row corresponds witha “female” constraint.

Elements of the granular data 806 collected by the source regiondeterminer 204 represent scaling values or weights that are inverses ofpercentages of recorded impressions associated with demographicconstraints recorded for those demographic constraints. For example, thedata stored in the example source region database 138 indicate that 50%(e.g., 0.5 in decimal form) of impressions associated with the “male”constraint are recorded as being associated with the “male” constraint,and 75% (e.g., 0.75 in decimal form) of impressions associated with the“female” constraint are recorded as being associated with the “female”constraint. Thus, in such examples, the example granular data 806determined by the example source region determiner 204 includes ascaling value or weight of ‘2’ (i.e., the inverse of 0.5) in the firstrow associated with the “male” constraint and includes a scaling valueor weight of ‘1.33’ (i.e., the inverse of 0.75) in the second rowassociated with the “female” constraint.

In the illustrated example, the target region calculator 206 utilizesminimum cross entropy to calculate or determine the target regiongranular data 214 that includes scaling values or weights for theexample target region 502 to account for non-counts orunder-representations when determining quantities of impressionsassociated with the demographic constraints of interest. The targetregion calculator 206 of the illustrated example utilizes the minimumcross entropy to determine the target region granular data 214 thatincludes a scaling value or weight for the “male” demographic constraintand a scaling value or weight for the “female” demographic constraint.To compensate for non-count or under-representation of impressions, theexample target region calculator 206 applies (e.g., multiplies, scalesup) the weights determined via the calculated minimum cross entropy tothe aggregate demographics data 802 to determine quantities (e.g.,counts, percentages) of the total impression count of the exampleaggregate impression data 804 that are recorded for the demographicconstraints of interest. For example, the target region calculator 206multiplies the determined weight value for the “male” demographicconstraint by ‘10’ to determine a portion of the 50 impressions of thetarget region 502 that are associated with males and multiplies thedetermined weight value for the “female” demographic constraint by ‘15’to determine a portion of the 50 impressions of the target region 502that are associated with females.

Thus, the example demographics estimator 140 of FIGS. 5 and/or 8 enablesthe example AME 106 or other entity to utilize the minimum cross entropyto calculate the granular data 214 of the example target region 502based on the example aggregate demographics data 802 and the exampleaggregate tuning data 802 of the target region 502, thereby reducing anamount of data collected from the target region 502 by computernetworked data collection systems. As a result, the example demographicsestimator 140 enables the example AME 106 or other entity to overcomenon-count or under-representation of impressions when determiningportions of recorded impressions for online activity that are associatedwith demographic constraints of interest.

FIG. 9 is a block diagram of an example processor platform 900structured to execute the instructions of FIGS. 3 and/or 4 to implementthe demographics estimator 140 of FIGS. 1, 2, 5, and/or 8. The processorplatform 900 can be, for example, a server, a personal computer, amobile device (e.g., a cell phone, a smart phone, a tablet such as aniPad™), a personal digital assistant (PDA), an Internet appliance, a DVDplayer, a CD player, a digital video recorder, a Blu-ray player, agaming console, a personal video recorder, a set top box, or any othertype of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer. The processor 912 of the illustratedexample includes the example target region determiner 202, the examplesource region determiner 204, the example target region calculator 206and/or, more generally, the demographics estimator 140.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 920 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

Coded instructions 932 of FIGS. 3 and/or 4 may be stored in the massstorage device 928, in the volatile memory 914, in the non-volatilememory 916, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus and articles of manufacture enable an audiencemeasurement entity to reduce an amount of computer memory and computerprocessing resources of computer networked data collection systemsutilized to collect data of a target region by enabling an audiencemeasurement entity to utilize minimum cross entropy to calculategranular data of a target region based on aggregate data of the targetregion and granular data of a source region. For example, by utilizingminimum cross entropy, the above disclosed methods, apparatus andarticles of manufacture enable the audience measurement entity tocalculate the granular data of the target region without having toimplement complex processes for gathering detailed behavioral anddemographics data from regions having small populations.

Further, the above disclosed methods, apparatus and articles ofmanufacture enable an audience measurement entity to utilize minimumcross entropy to calculate granular exposure data of a non-panelistregion based on tuning data collected (e.g., tuning event data collectedvia computerized media presentation devices connected to a computernetwork that facilitates presentation of media) from households of thenon-panelist region. Additionally or alternatively, the above disclosedmethods, apparatus and articles of manufacture enable an audiencemeasurement entity to utilize minimum cross entropy to calculategranular impressions data for online activity of a population includingnon-panelists based on aggregate impressions data (e.g., recorded onlineactivity data collected via computerized media presentation devicesconnected to a computer network that facilitates presentation of media)of the population. Thus, it will be appreciated that the above disclosedmethods, apparatus and articles of manufacture reduce processingresource utilization to compute a media audience measurement of thetarget region by utilizing minimum cross entropy to use data collectedfrom the computerized media presentation devices via the computernetwork without collecting person-specific data from members of thetarget region.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus for calculating granular data of anaudience of a target region, the apparatus comprising: a means forutilizing minimum cross entropy to apportion aggregate demographics dataand aggregate behavioral data of the target region based on granulardata of a source region; and a means for reducing an amount of computermemory resources by calculating the granular data of the audience of thetarget region without collecting person-specific data from audiencemembers in the audience of the target region, the granular data of theaudience of the target region including (A) a count of a first subset ofthe audience of the target region satisfying a behavioral constraint anda first demographic constraint and (B) a count of a second subset of theaudience of the target region satisfying the behavioral constraint and asecond demographic constraint, the calculated granular data of theaudience of the target region based on the apportioned aggregatedemographics data of the target region, the apportioned aggregatebehavioral data of the target region, and the granular data of thesource region.
 2. The apparatus as defined in claim 1, wherein theapportioned aggregate behavioral data of the target region includestuning data of the target region and the granular data of the sourceregion includes exposure data of the source region, the target regionbeing a non-panelist region and the source region being a panelistregion, the non-panelist region and the panelist region being mutuallyexclusive.
 3. The apparatus as defined in claim 1, wherein the aggregatebehavioral data of the target region is non-person-specific data of anon-panelist region that indicates a quantity of members of thenon-panelist region that satisfy the behavioral constraint, and theaggregate demographic data of the target region is non-person-specificdata of the non-panelist region that indicates a quantity of members ofthe non-panelist region that satisfy the demographic constraints.
 4. Theapparatus as defined in claim 1, wherein the calculation of the granulardata of the audience of the target region further includes means forperforming non-linear optimization based on at least one of (A) sharedbehavioral characteristics in the granular data of the source region andin the aggregate behavioral data of the target region, or (B) shareddemographic characteristics in the granular data of the source regionand in the aggregate demographics data of the target region.
 5. Theapparatus as defined in claim 1, wherein the calculation of the granulardata of the audience of the target region further includes: means forperforming non-linear optimization based on the granular data of thesource region, the aggregate demographics data of the target region, andthe aggregate behavioral data of the target region; and means fordefining an optimization constraint based on the aggregate behavioraldata and the aggregate demographics data of the target region, thenon-linear optimization being limited by the optimization constraint. 6.The apparatus as defined in claim 1, further including means forincreasing accuracy of values of the granular data of the audience ofthe target region by applying the minimum cross entropy to reducevariability of the values of the audience of the target region, theincreasing accuracy means to cause greater differences in intermediatevalues as compared to values of the granular data of the audience of thetarget region proximate to at least one of 0% or 100%, the intermediatevalues proximate to 50%.
 7. The apparatus as defined in claim 1, whereinthe first demographic constraint and the second demographic constraintare mutually exclusive.
 8. A computer implemented method to calculategranular data of an audience of a target region, the computerimplemented method comprising: utilizing, by executing an instructionwith a processor, minimum cross entropy to apportion aggregatedemographics data and aggregate behavioral data of the target regionbased on granular data of a source region; and reducing, by executing aninstruction with the processor, an amount of computer memory resourcesby calculating the granular data of the audience of the target regionwithout collecting person-specific data from audience members in theaudience of the target region, the granular data of the audience of thetarget region including (A) a count of a first subset of the audience ofthe target region satisfying a behavioral constraint and a firstdemographic constraint and (B) a count of a second subset of theaudience of the target region satisfying the behavioral constraint and asecond demographic constraint, the calculated granular data of theaudience of the target region based on the apportioned aggregatedemographics data of the target region, the apportioned aggregatebehavioral data of the target region, and the granular data of thesource region.
 9. The method as defined in claim 8, wherein theapportioned aggregate behavioral data of the target region includestuning data of the target region, and the granular data of the sourceregion includes exposure data of the source region, the target regionbeing a non-panelist region and the source region being a panelistregion, the non-panelist region and the panelist region being mutuallyexclusive.
 10. The method as defined in claim 8, wherein the aggregatebehavioral data is non-person-specific data of a non-panelist regionthat indicates a quantity of members of the non-panelist region thatsatisfy the behavioral constraint, and the aggregate demographic data isnon-person-specific data of the non-panelist region that indicates aquantity of members of the non-panelist region that satisfy thedemographic constraints.
 11. The method as defined in claim 8, whereinthe calculation of the granular data of the audience of the targetregion further includes performing non-linear optimization based on atleast one of (A) shared behavioral characteristics in the granular dataof the source region and in the aggregate behavioral data of the targetregion, or (B) shared demographic characteristics in the granular dataof the source region and in the aggregate demographics data of thetarget region.
 12. The method as defined in claim 8, wherein thecalculation of the granular data of the audience of the target regionfurther includes: performing non-linear optimization based on thegranular data of the source region, the aggregate demographics data ofthe target region, and the aggregate behavioral data of the targetregion; and defining an optimization constraint based on the aggregatebehavioral data and the aggregate demographics data of the targetregion, the non-linear optimization being limited by the optimizationconstraint.
 13. The method as defined in claim 8, wherein the accuracyof values of the granular data of the audience of the target region areincreased by applying the minimum cross entropy to reduce variability ofthe values of the audience of the target region and to cause greaterdifferences in intermediate values as compared to values of the granulardata of the audience of the target region proximate to at least one of0% or 100%, the intermediate values proximate to 50%.
 14. The method asdefined in claim 8, wherein the first demographic constraint and thesecond demographic constraint are mutually exclusive.
 15. A tangiblecomputer readable storage medium to calculate granular data of anaudience of a target region, the tangible computer readable storagemedium comprising instructions which, when executed, cause a machine toat least: utilize minimum cross entropy to apportion aggregatedemographics data and aggregate behavioral data of the target regionbased on granular data of a source region; and reduce an amount ofcomputer memory resources by calculating the granular data of theaudience of the target region without collecting person-specific datafrom audience members in the audience of the target region, the granulardata of the audience of the target region including (A) a count of afirst subset of the audience of the target region satisfying abehavioral constraint and a first demographic constraint and (B) a countof a second subset of the audience of the target region satisfying thebehavioral constraint and a second demographic constraint, thecalculated granular data of the audience of the target region based onthe apportioned aggregate demographics data of the target region, theapportioned aggregate behavioral data of the target region, and thegranular data of the source region.
 16. The tangible computer readablestorage medium as defined in claim 15, wherein the apportioned aggregatebehavioral data of the target region includes tuning data of the targetregion and the granular data of the source region includes exposure dataof the source region, the target region being a non-panelist region andthe source region being a panelist region, the non-panelist region andthe panelist region being mutually exclusive.
 17. The tangible computerreadable storage medium as defined in claim 15, wherein the aggregatebehavioral data is non-person-specific data of a non-panelist regionthat indicates a quantity of members of the non-panelist region thatsatisfy the behavioral constraint, and the aggregate demographic data isnon-person-specific data of the non-panelist region that indicates aquantity of members of the non-panelist region that satisfy thedemographic constraints.
 18. The tangible computer readable storagemedium as defined in claim 15, wherein the calculation of the granulardata of the audience of the target region further includes performingnon-linear optimization based on at least one of (A) shared behavioralcharacteristics in the granular data of the source region and in theaggregate behavioral data of the target region, or (B) shareddemographic characteristics in the granular data of the source regionand in the aggregate demographics data of the target region.
 19. Thetangible computer readable storage medium as defined in claim 15,wherein the instructions, when executed, further cause the machine to:calculate the granular data of the audience of the target region by:performing non-linear optimization based on the granular data of thesource region, the aggregate demographics data of the target region, andthe aggregate behavioral data of the target region; and defining anoptimization constraint based on the aggregate behavioral data and theaggregate demographics data of the target region, the non-linearoptimization being limited by the optimization constraint.
 20. Thetangible computer readable storage medium as defined in claim 15,wherein the accuracy of values of the granular data of the audience ofthe target region are increased by applying the minimum cross entropy toreduce variability of the values of the audience of the target regionand to cause greater differences in intermediate values as compared tovalues of the granular data of the audience of the target regionproximate to at least one of 0% or 100%, the intermediate valuesproximate to 50%.